# The Blue Connection. A Systematic and Critical
## Review of Shipping Network Research

Marc-Antoine Faure, César Ducruet

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Marc-Antoine Faure, César Ducruet. The Blue Connection. A Systematic and Critical Review of Shipping Network Research. Networks and Spatial Economics, 2025, 10.1007/s11067-025-09705-y. hal-05286865

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# The Blue Connection

## A Systematic and Critical Review of Shipping Network Research

May 13, 2025

### Abstract

Despite its early development by geographers, research on shipping networks long remained a narrow confine of academia. Since the late 2000s, however, this subfield is experiencing rapid growth, fueled by increased shipping data availability, computational power, renewed graph-theoretical methods, and a growing recognition that maritime connectivity matters for trade and socio-economic development. This research provides a bibliometric analysis of no less than 329 peer-reviewed articles published between 2007 and 2025. First, it describes the gathered corpus from diverse angles, such as the growth of papers, the main journals, its disciplinary background, and the pattern of coauthorships. Second, we use a natural language processing (NLP) approach, namely the structural topic model, to identify four main topics: trade and connectivity; hubs and centrality; vulnerability and robustness; and communities and spatial structure. Three emerging subthemes are identified, including environmental impacts, urban and regional development, and the strategies of shipping firms and alliances.

**Keywords:** bibliometric analysis; complex networks; graph theory; maritime transport; shipping network; social network analysis; structural topic modeling
# Introduction

In a world where more than 80% of global trade volumes are supported by sea transport (UNCTAD, 2024), maritime connectivity plays an essential role in socio-economic development (Gallup et al., 1999; Lane and Pretes, 2020). Such a fact is familiar to human geographers, for whom ports and maritime transport are useful to "take the pulse of world trade and movement" (Ullman, 1949). Despite this importance, network analysis has been used relatively late to study maritime flows, compared to other networks, such as roads and railways (Ducruet and Beauguitte, 2014). After a long phase made of relatively scattered contributions, port and maritime research integrated graph theory, social network analysis, and the complex network framework in the late 2000s, leading to an exponential growth of studies of the kind in the 2010s and 2020s.<fnref n="1" /> What have been the main themes and methods of shipping network research since the late 2000s? Did the integration of new analytical methods change the understanding of ports and shipping? How did shipping network research contribute to network science in general?

It is important to note that the integration of network-analytical methods in port and maritime research has been gradual, to such an extent that reviews of the field did not pay attention to such an evolution.<fnref n="2" /> Experts in maritime logistics (Panayides 2006) and maritime economics (Panayides and Cullinane 2002; Heaver 2006) did not even mention the concept of 'network' in their respective reviews, like those of Woo et al. (2011), Pallis et al. (2011), Woo et al. (2013), and Notteboom et al. (2013). Another example is the review by Talley (2013) of the literature published in the journals *Maritime Policy and Management* and *Maritime Economics and Logistics* since 2000, highlighting shipping/port performance, management, and finance as the most important themes. As shown later in this article, shipping network analysis was still in its infancy at this stage. Subsequent reviews, such as those of Shi and Li (2017) and Bai et al. (2021), barely mentioned the concept of network, compared to other topics such as intermodal logistics, freight rates, port efficiency, economic impact, shipping finance, regional development, operations and management. The same applies to other general reviews of port and maritime transport research, such as those of Xu et al. (2021), Munim and Saeed (2019), Chen et al. (2018), Wang and Peng (2023), and Dragović et al. (2024).

More recent and specific reviews, such as Xiao et al. (2024b) on artificial intelligence;

<footnote n="1">¹We would like to draw the reader's attention to one important point: the blue references refer to the main bibliography of the article. Uncolored references refer to the corpus of articles used in this review. This corresponds to the appendix C.</footnote>

<footnote n="2">²Section 2 details the constitution of the corpus.</footnote>
Zhou, Li, and Yuen (2023), Xiao et al. (2024a) and Qi et al. (2024) on sustainable transport and environmental issues<fnref n="3" />; Ghorbani et al. (2022) and Chen et al. (2022) on shipping alliances; Filom, Amiri, and Razavi (2022) and Wang and Peng (2023) on port operations, did not identify the shipping network as an emerging topic or field.<fnref n="4" />

The contribution of geographers was to build bridges between human geography and transport studies (Ng and Ducruet 2014; Ng et al. 2014). In particular, Ng and Ducruet (2014) identified two topics related to networks, namely ‘port connectedness’ and ‘ports’ place in shipping strategies and networks’. Lau et al. (2017) reviewed 294 articles published about container shipping between 1967 and 2013 based on title words, acknowledging the importance of ‘network design’ and ‘connectivity’ in the corpus. Ducruet et al. (2019) reviewed articles published in *Journal of Transport Geography* as well as geography and non-geography journals, highlighting the growing integration of network analysis in port and maritime research. Ducruet (2020a) proposed four arguments explaining the late emergence of shipping network analysis: the lack of shipping data, the continental culture of scholars focusing on land masses and hinterlands, the consideration of seas and oceans as ‘empty spaces’, and the competitiveness of air transport for passenger travel since the second half of the 20th century. Ducruet and Notteboom (2023)) found that maritime connectivity has recently become an important component of port system research.

In general, the application of network analysis to maritime transport research has hardly been discussed in the aforementioned reviews, despite the parallel growth of related publications. This is rather surprising, given early discussions on the absence of port centrality measures (De Langen, Van der Lugt, and Eenhuizen 2002; de Langen, Nidjam, and Van der Horst 2007) and the position of ports in so-called scale-free shipping networks (Le Marchand 2000; Foschi 2002).<fnref n="5" /> Other examples include Bergantino and Veenstra (2002) and Caschili and Medda (2012), focusing on maritime transport as a ‘complex system’. Reviews dedicated to the analysis of shipping networks gradually emerged, each focusing on one specific aspect: (Ansorena 2018) on bilateral connectivity in liner shipping networks, Kanrak, Nguyen, and Du (2019) on methods of shipping network analysis, Álvarez, Adenso-Díaz, and Calzada-Infante (2021) on the complex network framework applied to shipping research, (Munim et al. 2020) on innovation in

<footnote n="3">³There is a flourishing literature in the past three years reviewing academic research on shipping and environmental issues, we thus selected some articles without seeking to be exhaustive.</footnote>

<footnote n="4">⁴The literature in operations research that deals with port efficiency, scheduling, containers' operations etc. is extremely large. For example, Weerasinghe, Perera, and Bai (2024) reviewed 1,768 articles related to container terminal operations</footnote>

<footnote n="5">⁵Class of network characterized by a few large hubs and a majority of small and secondary nodes.</footnote>
the domain of big data, (Wendler-Bosco and Nicholson 2020; Nguyen et al. 2023; Zhang et al. 2024) on port and maritime disruptions, (Wang et al. 2021) on port dominance evaluation methods, (Liu et al. 2021; Shankar et al. 2022) on visualization of vessel trajectory data, and Yue and Mangan (2024) on the reliability of maritime container transport networks.

The field of shipping networks therefore needs an up-to-date and comprehensive review. Our study is based on a corpus of no fewer than 329 articles published in peer-reviewed journals between 2007 and 2025⁶. Such articles have in common the term ‘maritime network’ or ‘shipping network’ in their title and/or abstract, as well as possible variants (e.g., ‘maritime transport network’). Several other criteria guided the selection of articles. First, they are all empirical studies of real-world shipping networks, contrary to the theoretical literature in operations research and engineering, which uses mathematical programming and modeling to optimize ship routing, scheduling, and hub location<fnref n="7" />. Second, we excluded the works in history, archaeology, anthropology, and linguistics dealing with shipping networks because such disciplines have a more qualitative approach. Other works such as tactical networks in maritime warfare were also excluded.

The remainder of this article is developed as follows. Section 1 reviews the pioneering literature on shipping networks, published before 2007, and discusses its legacy for subsequent decades. Section 2 introduces the corpus gathered between 2007 and 2025 and describes its evolution from various angles, such as the growth of articles, the main academic journals represented, its disciplinary background, and the geographic specialization of studies. Section 3 dives into the contents of the corpus through a text mining/NLP analysis of abstracts, to reveal semantic patterns and trends over the period. Section 4 offers a critical review of the four main themes of the corpus, while we discuss in Section 5 the “missing links” that appear in our analysis. The last section concludes on further research pathways for both maritime transport studies and network science.

# 1. The Legacy of Pioneering Studies (1968-2007)

The relational approach in port and maritime research has long consisted in the analysis of “port systems”, a core concept in port and maritime geography originating from the

<footnote n="6">⁶This choice makes it possible to have a structured corpus (the abstracts of articles). In addition, limiting ourselves to articles published in journals makes disciplinary classification easier and more transparent.</footnote>

<footnote n="7">⁷See in-depth reviews by Tran and Haasis (2015) and Christiansen et al. (2020).</footnote>
work of Taaffe, Morrill, and Gould (1963) on the evolution of transport corridors and related port hierarchies in West Africa. Due to the strong emphasis such research on hinterlands, Rimmer (1967a,b) argued in favor of the integration of maritime links between ports. There had been a recognition that forelands matter as much as hinterlands for the understanding of port activities (Weigend 1952; Britton 1965; Robinson 1970) notably through the concept of “port triptych” (hinterland-port-foreland) (Vigarié 1979).

But empirical studies of forelands consisted mainly in measuring the share and specialization of connections, without a network approach per se, despite the wide use of graph theory in geography and regional science since the early 1950s (Ducruet and Beauguitte 2014). The application of graph theory to a maritime network was first proposed by Ross Robinson (1968) in his Ph.D. dissertation. He mapped the connections among British Columbia ports to illustrate the central position of Vancouver in the system. Despite its innovative character, his work was never published in a journal and was not cited subsequently. Recalling the main steps of his career evolution, Robinson (2015) recognized that port research at that time had to adapt to changing contexts by becoming more applied and practical.

Only about 15 years later, other analyses were proposed. Marti (1981) described the pattern of US-Canada container flows, while Marti and Krausse (1983) and Helmick (1994) searched for load center development in the North Atlantic liner shipping network. The works of McCalla (1986) on Canadian coastal shipping and Dick and Rimmer (1993) on Asia-Pacific container flows explicitly made use of network analysis, the first using graph theory and the latter arguing that such methods should be revived. The work of Joly (1999) constitutes the first analysis of the global container shipping network, using graph theory. Like Robinson's work, ports were characterized by their connectivity (degree) rather than throughput, while the cartography of the network illustrated the dominance of East Asia. Subsequent analyses focused on the strategies of shipping lines and alliances, mapping regional networks (McCalla 2004; McCalla, Slack, and Comtois 2004, 2005; Veenstra, Mulder, and Sels 2005) or individual port networks (Comtois and Wang 2003; Rimmer and Comtois 2005; Frémont and Soppé 2004).

In general, shipping network research in the early period constitutes a minor field, mostly invested by geographers. It remained scattered, with long periods of inactivity, and methodological innovations were introduced relatively late compared to research on other (transport) networks. The most innovative works had been unpublished Ph.D. dissertations, working papers, or conference proceedings, remaining relatively unknown to date. The goal of the next sections is to analyze how shipping network research has
been conducted in the more recent period, to the point of constituting a field in its
own right. Given the subsequent exponential growth of related studies, we decided to
approach this in a systematic and quantitative way.

## 2. Describing the Corpus

### 2.1. Size evolution

By late December 2024, the number of articles dealing with shipping networks had reached a total of 329 items, including nine articles for the year 2025⁸. As shown in Figure 1, the first decade (2007-2017) is characterized by a very modest publication rate, with an average yearly number of 5.5 articles. A second period can be defined as the “take-off”, with an average number of 23.5 articles published between 2018 and 2021. If we merge 2024 and 2025, the late period 2022-2025 is characterized by an average number of no less than 55 articles. The year 2023 marks an exception, with only 28 articles, compared to 63 and 74 articles in 2022 and 2024, respectively. Multiple checks have ensured that such a drop was not caused by missing references. In general, this evolution is characterized by rapid growth in corpus size. This reflects the growing availability of shipping data, the popularization of network-analytical methods in the field of maritime research, and the increased recognition that sea transport is a vital component of global trade and economic development.

<footnote n="8">⁸We stopped collecting articles on 15 January 2025. The gap between the other (complete) years and the year 2025 will not pose a problem for the estimation of the structural topic model in section 3 because we are grouping the years into periods: 2007 - 2019 and 2020-2025 (see section 2.3 for details on the methodology).</footnote>
<figure><img src="image_3.png" /><figcaption>FIGURE 1. Evolution of the number of articles over time (2007 - 2025)</figcaption></figure>

*Notes*: We plot the number of articles per year. We observe a significant increase of publications starting in 2017. We included 2025 as we then analyse periods and not only years.

## 2.2. Disciplinary Background

The distribution of articles by journal (Table 1) and journal background (Table 2) offers useful evidence on the disciplinary and thematic focus of the corpus. The leading journal is *Journal of Transport Geography* (JTRG) which, contrary to other journals, is both disciplinary (geography) and thematic (transport) (Ducruet et al. 2019). This importance confirms the early role of geographers in the analysis of shipping networks, as discussed in Section 2. Although *Geojournal* is the only other geography journal in Table 1, geography as a whole dominates other fields in Table 2, although it is slightly below “shipping” and “transport & logistics” in the second period. This analysis may underestimate the real importance of geography, since (transport) geographers are increasingly publishing in transport journals, in their quest to better address operational logics, while leaving behind core issues of human geography (Ng and Ducruet 2014).
<table><caption>TABLE 1. Number of Articles by Journal and Period</caption><thead><tr><th>Journal</th><th>2007-2017</th><th>2018-2025</th><th>Total</th></tr></thead><tbody><tr><td>Journal of Transport Geography</td><td>12</td><td>22</td><td>34</td></tr><tr><td>Maritime Policy and Management</td><td>5</td><td>23</td><td>28</td></tr><tr><td>Ocean and Coastal Management</td><td>-</td><td>16</td><td>16</td></tr><tr><td>Maritime Economics & Logistics</td><td>5</td><td>9</td><td>14</td></tr><tr><td>Sustainability</td><td>1</td><td>12</td><td>13</td></tr><tr><td>Transport Policy</td><td>2</td><td>9</td><td>11</td></tr><tr><td>Journal of Marine Science and Engineering</td><td>-</td><td>9</td><td>9</td></tr><tr><td>Physica A</td><td>2</td><td>6</td><td>8</td></tr><tr><td>Transportation Research Part E</td><td>4</td><td>4</td><td>8</td></tr><tr><td>Networks and Spatial Economics</td><td>1</td><td>6</td><td>7</td></tr><tr><td>Transportation Research Part D</td><td>-</td><td>7</td><td>7</td></tr><tr><td>GeoJournal</td><td>3</td><td>2</td><td>5</td></tr><tr><td>International Journal of Shipping and Transport Logistics</td><td>-</td><td>5</td><td>5</td></tr></tbody></table>

*Maritime Policy and Management* (MPM) as well as *Maritime Economics & Logistics* (MEL) can be considered the economic counterparts of JTRG, being the representative journals of the *International Association of Maritime Economists* (IAME). Together, they constitute the category of “shipping”, which is equal to geography for the whole period. They are closely followed by general transport journals, which are not specialized in shipping, while general economic journals have the lowest number of articles (3). This means that shipping network research in geography and economics is mostly published in specialized field-journals. Physics was important in the early stage and maintained its rank afterwards. Most of the other papers were published in transdisciplinary journals, especially in the second period, such as marine sciences, geosciences, environment and energy, and networks and complex systems. The category “multidisciplinary” includes journals such as *Sustainability*, *Nature Scientific Reports*, *Nature Communications*, *PNAS*, and *Plos One*, which cover a very wide diversity of topics. In general, the corpus can be divided between transport studies (144) and other studies (185), and its academic background has widened over time.

Table 3 presents a different distribution of articles, this time by geographic area of study. The global nature of maritime networks is well represented, with world-level
<table><caption>TABLE 2. Classification of Articles by Journal Background (2007–2025)</caption><thead><tr><th>Background</th><th>2007-2017</th><th>2018-2025</th><th>Total</th></tr></thead><tbody><tr><td>Geography</td><td>20</td><td>37</td><td>57</td></tr><tr><td>Shipping</td><td>11</td><td>46</td><td>57</td></tr><tr><td>Transport &amp; logistics</td><td>11</td><td>43</td><td>54</td></tr><tr><td>Marine sciences</td><td>-</td><td>38</td><td>38</td></tr><tr><td>Engineering &amp; data sciences</td><td>3</td><td>31</td><td>34</td></tr><tr><td>Multidisciplinary</td><td>5</td><td>25</td><td>30</td></tr><tr><td>Physics &amp; natural sciences</td><td>7</td><td>10</td><td>17</td></tr><tr><td>Environment &amp; energy</td><td>-</td><td>16</td><td>16</td></tr><tr><td>Geosciences</td><td>-</td><td>13</td><td>13</td></tr><tr><td>Networks &amp; complex systems</td><td>1</td><td>11</td><td>12</td></tr><tr><td>Economics</td><td>-</td><td>3</td><td>3</td></tr><tr><td><strong>Total</strong></td><td>60</td><td>269</td><td>329</td></tr></tbody></table>

studies occupying half of the publications in the first period, one-third in the second period, and a little less than one-third in the total. This is followed by an overwhelming importance of studies in relation to Asia. In particular, studies of the Maritime Silk Road (MSR) emerged in the second period, concentrating 10% of all studies, namely 20% when combined with China, and almost 30% together with the rest of Asia. Taken altogether, North and South America stagnated around 5-6 articles, while Europe witnessed a jump from 3 to 19 papers between the two periods, therefore ranking 5th in the total. Some categories went through a drastic increase in the second period, like the Arctic, Europe-Asia, Oceania/Pacific, and the Middle East.

## 2.3. Structural Topic Modeling

To complement the description of corpus evolution, we build a corpus of abstracts for all the collected papers. Using this corpus and the associated metadata, we estimate a series of structural topic models (henceforth STM) with two main objectives: explore the topical structure of shipping network research and estimate the impact of some variables, such as the period, the field, or the affiliation of authors on the proportion of each topic.

Text is frequently used as data by social science researchers (Bail 2014; Lindstedt 2019), both through qualitative (surveys, interviews) and quantitative studies. With the rapid digitization of academic resources, a large amount of metadata has been
<table><caption>TABLE 3. Classification of Articles by Geographic Area (2007–2025)</caption><thead><tr><th>Area</th><th>2007–2017</th><th>2018–2025</th><th>Total</th></tr></thead><tbody><tr><td>World</td><td>31</td><td>95</td><td>126</td></tr><tr><td>Rest of Asia</td><td>8</td><td>30</td><td>38</td></tr><tr><td>China</td><td>6</td><td>28</td><td>34</td></tr><tr><td>Maritime Silk Road</td><td>–</td><td>26</td><td>26</td></tr><tr><td>Europe</td><td>3</td><td>19</td><td>22</td></tr><tr><td>Americas</td><td>5</td><td>6</td><td>11</td></tr><tr><td>Arctic</td><td>–</td><td>10</td><td>10</td></tr><tr><td>Europe-Asia</td><td>1</td><td>6</td><td>7</td></tr><tr><td>Oceania/Pacific</td><td>1</td><td>6</td><td>7</td></tr><tr><td>Africa</td><td>2</td><td>3</td><td>5</td></tr><tr><td>Middle East</td><td>–</td><td>4</td><td>4</td></tr><tr><td>Atlantic</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Antartica</td><td>–</td><td>1</td><td>1</td></tr><tr><td>Non-specified</td><td>2</td><td>34</td><td>36</td></tr><tr><td>Total</td><td>60</td><td>269</td><td>329</td></tr></tbody></table>

made available to researchers interested in the evolution of scientific production. Topic modeling is a set of methods that aim to discover “hidden” topics in text data (Lindstedt 2019). Mixed-membership topic modeling has been first developed by Blei, Ng, and Jordan (2003), and is usually designated as ‘Latent Dirichlet Allocation’ (LDA). Blei, Ng, and Jordan (2003) assumed that each document in the corpus is a mixture of topics and each topic is a distribution of words. The second step in the development of topic modeling (Lindstedt 2019) is the “structural topic model” (Roberts et al. 2013, 2014), which was developed to overcome one of the main limitations of topic modeling via LDA: topics were assumed to be independent within each document, which is rarely the case when dealing with complex documents. In STM, the topic proportions for the documents are drawn from a logistic-normal distribution. This approach takes into account the dependence between topic distributions and allows for the inclusion of covariates in the estimation. For each document in the corpus (i.e., each article abstract) we define a data generating process and then use the data to find the most likely values for the parameters. The generative process for each document *d* with a vocabulary of size *V* and *K* topics can be divided into three blocks (Roberts, Stewart, and Tingley 2019):

a. $\theta_d$ is drawn from a logistic-normal generalised linear model based on a vector of
document covariates $X_d$:

$$ (1) \qquad \theta_d | X_{d\gamma}, \Sigma \sim \text{Logistic-Normal} \left( \mu = X_{d\gamma}, \Sigma \right) $$

With $X_d$ a $1 \times p$ vector, $\gamma$ a $p \times K-1$ matrix of coefficients and $\Sigma$ a $K-1 \times K-1$ covariance matrix.

b. The model estimates the document-specific distribution over words for each topic is given by:

$$ (2) \qquad \beta_{d,k} \propto \exp \left( m + \kappa_k^{(t)} + \kappa_{y_d}^{(c)} + \kappa_{y_d,k}^{(i)} \right) $$

Where $m$ is the baseline word distribution in the document, $\kappa_k^{(t)}$ is the topic-specific deviation, $\kappa_{y_d}^{(c)}$ the covariate group deviation and $\kappa_{y_d,k}^{(i)}$ the interaction<fnref n="9" />. We use STM to explore the corpus at two levels. At the global level, we want to understand how the shipping network literature is structured i.e., labelling the topics; at the topic-level we want to estimate the impact of a basic set of covariates on topic proportion. This second objective allows us to observe and quantify the evolution of topics’ popularity over time, and the geographical and field determinants of each topic.

# 3. Model Estimation

## 3.1. Selecting the Number of Topics

Before presenting the results of our quantitative analysis of shipping network studies, let us briefly discuss model selection, focusing particularly on determining the “desirable” number of topics. The development of automated text analysis came with the attractive promise of considerably reducing the costs of analyzing large amount of text data (Grimmer and Stewart 2013). This potential is associated with a non-negligible pitfall: the trade-off between “statistical” quality - coming from quantitative diagnoses - and human-based interpretability (Lindstedt 2019). To be more precise, there is no consensus on the means for selecting the number of topics that produces both interpretable results and “optimal” properties (Roberts et al. 2014; Farrell 2016; Lindstedt 2019; Roberts, Stewart, and Tingley 2019). The theoretical and empirical framework developed by Roberts et al.

<footnote n="9">⁹For additional details, one can refer to Roberts, Stewart, and Tingley (2019) (with applications in R); Brunetti, Joëts, and Mignon (2024) and Candelon, Joëts, and Mignon (2024) for an application of STM to review the econometric literature.</footnote>
(2013, 2014), Roberts, Stewart, and Airoldi (2016) and Roberts, Stewart, and Tingley (2019)<fnref n="10" /> provide some directions on the selection of the number of topics¹¹ and the in-depth analysis of topics' structures and determinants.

The first metric, computed in Fig. 2 for 5 to 15 topics, is the 'semantic coherence' (Mimno et al. 2011). This criterion is maximized when the most probable words in a topic are also co-occurring together in the documents. This criterion is computed as follows:

$$ (3) \qquad C_k = \sum_{i=2}^{M} \sum_{j=1}^{i-1} \log \left( \frac{D(v_i, v_j) + 1}{D(v_j)} \right) $$

Roberts et al. (2014) show that semantic coherence is biased by the fact that it is relatively easy to reach a high value when considering few topics with dominant words. To deal with this issue and improve topic partition, they propose to use the FREX metric (Bischof and Airoldi 2012; Airoldi and Bischof 2016). This second criterion accounts for the frequency *and* the exclusivity of words to topics. The FREX is given by the following expression:

$$ (4) \qquad \text{FREX}_{k,v} = \left( \frac{\omega}{\text{ECDF}\left(\beta_{k,v} / \sum_{j=1}^{K} \beta_{j,v}\right)} + \frac{1-\omega}{\text{ECDF}\left(\beta_{k,v}\right)} \right)^{-1} $$

In the equation 4 'ECDF' is the empirical CDF and $\omega$ is the weight<fnref n="12" />. Figure 2 shows three other metrics associated with the set of topics tested: held-out likelihood, residuals, and lower bound. The evolution of these different metrics also illustrates the difficulty of choosing the number of topics that satisfies both the consistency and statistical quality of the model. After various qualitative analyses of the different partitions, we decided to segment the corpus into five topics; this partition corresponds to the most coherent structure.

There are several reasons for choosing a limited number of topics. Firstly, if we look at Figure 2, this is a number of topics with the greatest semantic consistency. We end up with fairly large topics dominated by a few key words, which tend to generate this

<footnote n="10">¹⁰We use the `stm` package to perform the quantitative analysis of the corpus. The most recent version of the R-package can be found here: [here](https://CRAN.R-project.org).

¹¹This issue is also well-discussed, with some additional guidelines and good practices in Lindstedt (2019).</footnote>

<footnote n="12">¹²Roberts, Stewart, and Tingley (2019) fixed $\omega$ to 0.7 to favor 'exclusivity'.</footnote>
type of result. In the case of a literature review, it seems to us that this also corresponds
to what is expected of a score: to identify the major trends that structure the field and
its division into sub-fields. This argument is reinforced by the fact that our corpus
is relatively small, with 329 articles, and composed of elements that are themselves
small in size, since they are abstracts. Blei (2012), who review 17,000 academic articles
published in *Science*, limit themselves to 50 to 100 topics. Roberts et al. (2014) support
this point by insisting on the importance of the size and variety - or what the researcher
considers it to be - of the corpus on the choice of the number of topics.

Beyond the number of elements in the corpus and their length, we also defend the
idea that the field of shipping networks being relatively young (less than two decades) is
interesting to identify the major trends. These trends are structuring because they allow
us to understand the dynamics of the field and to identify the major research questions
that run through it: trade, resilience, environment, communities, ... For this reason, we
are limiting the number of topics. In the appendix B, the results are presented for 6
and 10 topics, but we lose clarity by allowing sub-themes to appear that are not relevant
to detach from the broader themes to which they are attached.
<figure><img src="image_4.png" /><figcaption>FIGURE 2. Diagnoses for 5-15 topics</figcaption></figure>

*Notes*: We ran a structural topic model for each number of topics from 5 to 15. From left to right and top to bottom, the figure plots the evolution of : semantic coherence, held-out likelihood, residuals and ‘lower bound’. We decided to focus on semantic coherence after analysing the output of each model¹³ strongly emphasise that these measures are imperfect and are no substitute for human validation of topics, in conjunction with subject consistency.

**3.2. Words and Topics**

The first output of the structural topic model is the partition of the corpus into topics. Four metrics are used to define each topic: the occurrences with the highest probability, the ‘FREX’, the ‘Lift’ and the Score.

The table A3 brings together the ten most frequent occurrences for each of the four metrics, and for each topic<fnref n="14" />. This partition then allows us to label the six topics. We propose the following labels:

a. Topic 1: cruises, arctic shipping and operations;

b. Topic 2: connectivity, bilateral trade and regional development;

<footnote n="14">¹⁴Truncated terms such as ‘cruis’, ‘resili’ or ‘connect’ bring together all the terms derived from this basic structure. For ‘cruis’ this could be ‘cruise’, ‘cruises’, ‘cruising’… allowing us to cover all the grammatical uses and functions of this term within a sentence</footnote>
c. Topic 3: container operations, hubs and Asian shipping networks;

d. Topic 4: resilience, vulnerability and risk mitigation;

e. Topic 5: spatial structure, network communities and port systems.

These five topics correspond to our intuitions: they suggest a disciplinary and thematic partition. First of all, they group together terms that are associated with the “premises” of the field: network structure, hubs, communities, connectivity. The analysis of shipping networks was made possible by advances in complex networks in the early 2000s, and the early years of the field are integrated into the issues arising from this work. There are also terms such as connectivity and bilateral trade, which are more closely related to work in international economics and economic geography<fnref n="15" />. This transition from topological and structural issues to economic and political research questions suggests a maturing of the field.

<figure><img src="image_5.png" /><figcaption>FIGURE 3. Top-10 most probable words for each topic</figcaption></figure>

*Notes*: The figure shows the most probable words for each topic. It is used to label the topics.

<footnote n="15">¹⁵The study of network resilience and vulnerability is a recent trend in the study of networks. We find both simulations of network 'attacks' and studies of real shocks, such as earthquakes and hurricanes (Rousset and Ducruet, 2020), the war in Ukraine (Polo-Martin et al., 2024; Ke et al., 2024) or the development of the maritime silk road and regional development associated with maritime activity (Liupeng et al., 2024)</footnote>
<table><caption>TABLE 4. Top Words from the STM model with 5 topics</caption><thead><tr><td>Topic</td><td>Highest Prob</td><td>FREX</td><td>Lift</td><td>Score</td></tr></thead><tbody><tr><td>1</td><td>cruis, ship, vessel, oper, rout, arctic, use, model, emiss, data</td><td>emiss, invas, itinerari, speci, cruis, arctic, water, environment, asr, passag</td><td>-call, acceler, aquat, arriv, bioinvas, classif, contact, ecosystem, emiss, flag</td><td>cruis, arctic, emiss, itinerari, asr, season, nis, flag, passag, invas</td></tr><tr><td>2</td><td>connect, trade, region, contain, transport, liner, flow, develop, use, effect</td><td>bilater, freight, north, export, inter-, vietnam, literatur, period, america, connect</td><td>-go, accept, accommod, add, adequ, agenda, amongst, best, bilater, boom</td><td>bilater, trade, vietnam, korean, inter-, capita, country', export, -go, lsbci</td></tr><tr><td>3</td><td>contain, central, hub, connect, studi, degre, competit, use, develop, index</td><td>hong, asian, kong, busan, central, degre, shanghai, cluster, competit, singapor</td><td>alphabet, bbv, believ, bohai, claim, colombo, coronavirus, cyclic, decemb, dissemin</td><td>busan, asian, competit, hong, kong, tier, between, rank, singapor, central</td></tr><tr><td>4</td><td>resili, vulner, disrupt, transport, impact, trade, studi, model, rout, attack</td><td>resili, failur, cascad, recoveri, oil, attack, disrupt, vulner, glsn, conflict</td><td>accid, aid, blockag, cancel, cascad, catastroph, confid, conflict, encounter, failur</td><td>resili, cascad, failur, attack, disrupt, glsn, reliabl, conflict, crude, recoveri</td></tr><tr><td>5</td><td>transport, trade, system, traffic, communiti, data, spatial, method, structur, use</td><td>communiti, msr, silk, road, tanker, bulk, traffic, detect, advantag, extract</td><td>assumpt, billion, bottleneck, communic, continuum, flexibl, gmn, greec, heavy-tail, insular</td><td>silk, communiti, msr, bulk, tanker, gmn, waypoint, advantag, road”, socioeconom</td></tr></tbody></table>
### 3.3. Covariates and Topic Proportion

We then estimate the impact of various covariates on the *i*'s topic proportion, which corresponds to the following equation:

$$ (5) \qquad y_i = \beta_0 + \phi' \mathbf{X}_i + \varepsilon_i $$

Where $y_i$ is the proportion of topic *i* in the structural topic model, $\beta_0$ the intercept and $\mathbf{X}_i$ a set of covariates. We included the period in which this article was published<fnref n="16" />, the majoritarian geographic area<fnref n="17" /> in the authors’ affiliations and the field to which the articles belong<fnref n="18" />.

The output of the regressions in Table A2 offers some interesting insights on the evolution of shipping network research. We first observe that topic proportion is significantly impacted by the transition from 2007 - 2019 to 2020 - 2025 for topics 1, 3 and 5. Topic 1 focuses on Arctic routes, operational challenges and pandemics, it is thus quite expected<fnref n="19" />. Topic 4 is also significantly and positively affected by the recent period : it includes research interests around resilience, vulnerability, disruption... which is correlated to pandemics and wars<fnref n="20" />.

We decided to consider “Shipping” as the reference for the background; this is a fairly broad and general category, which is well represented in terms of articles, allowing us to see how more specialised fields, which have also emerged more recently, position themselves in relation to ‘generalist-shipping’ research and journals. Shipping “background” includes journals such as *Maritime Economics and Logistics*, *Maritime Policy and Management* and *Journal of Shipping and Trade*. The traditional themes of shipping research (connectivity, trade, formation of hubs, etc.), which correspond to topics 2 and 3, are negatively associated with the other journals compared with shipping ones.

<footnote n="16">¹⁶The results are robust to the use of three periods (2007 - 2013, 2014 - 2019 and 2020 - 2025) and year dummies</footnote>

<footnote n="17">¹⁷When two zones are at par, we used the zone of the first author.</footnote>

<footnote n="18">¹⁸Determined from the journals in which they were published</footnote>

<footnote n="19">¹⁹In the remainder of this article, we have decided not to include a discussion of this topic. It is in fact limited to a few references (the Covid-19 theme is dealt with more extensively in the resilience topic) and with scattered concerns. Nevertheless, we wanted to mention it because of the interest it may present. See Lavissière, Sohier, and Lavissière (2020), Lin et al. (2023) and Qi et al. (2024) for reviews with a specific focus on arctic shipping.</footnote>

<footnote n="20">²⁰Polo-Martín et al. (2024) emphasised that networks’ vulnerability became a popular research theme in recent years, following major disruptions such as Covid-19, Ukrainian war and Red Sea crisis.</footnote>
<table><caption>TABLE 5. Regression Results for Topics 1–5 (Updated Model)</caption><thead><tr><th rowspan="2">Variable</th><th colspan="5">Topic</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>0.059801</td><td>0.51360***</td><td>0.18878***</td><td>0.01900</td><td>0.21754***</td></tr><tr><td>2020-2025</td><td>0.11202***</td><td>-0.07046*</td><td>-0.03721</td><td>0.10856**</td><td>-0.11237**</td></tr><tr><td>China</td><td>-0.07967*</td><td>-0.27018***</td><td>0.22455***</td><td>0.08668*</td><td>0.03921</td></tr><tr><td>North America</td><td>-0.03092</td><td>-0.14936</td><td>-0.00482</td><td>0.06353</td><td>0.12260</td></tr><tr><td>Rest of Asia</td><td>0.08880*</td><td>-0.24683***</td><td>0.29796***</td><td>-0.02784</td><td>-0.11254*</td></tr><tr><td>Economics</td><td>0.21921</td><td>-0.10118</td><td>-0.01826</td><td>0.00121</td><td>-0.10440</td></tr><tr><td>Engineering & data sciences</td><td>-0.03197</td><td>-0.16452*</td><td>-0.11152</td><td>0.14104*</td><td>0.16903*</td></tr><tr><td>Environment & energy</td><td>0.14341*</td><td>-0.17385*</td><td>-0.23243*</td><td>0.25379*</td><td>0.00905</td></tr><tr><td>Geography</td><td>0.00095</td><td>0.04062</td><td>-0.08237</td><td>0.00950</td><td>0.03170</td></tr><tr><td>Geosciences</td><td>0.07629</td><td>-0.16029*</td><td>-0.25321**</td><td>0.02982</td><td>0.30650**</td></tr><tr><td>Marine sciences</td><td>0.06481</td><td>-0.08556</td><td>-0.19272**</td><td>0.13309*</td><td>0.07990</td></tr><tr><td>Multidisciplinary</td><td>0.19237**</td><td>-0.12341*</td><td>-0.12722*</td><td>0.03063</td><td>0.02717</td></tr><tr><td>Networks & complex systems</td><td>0.02450</td><td>0.03949</td><td>-0.22858*</td><td>-0.05876</td><td>0.22638*</td></tr><tr><td>Physics & natural sciences</td><td>0.03590</td><td>-0.18884*</td><td>-0.03360</td><td>0.03880</td><td>0.14549</td></tr><tr><td>Transport & logistics</td><td>-0.06706</td><td>0.02116</td><td>-0.06936</td><td>0.11617*</td><td>-0.00152</td></tr></tbody></table>

Note: Columns (1), (2), (3), (4), and (5) show coefficients for Topics 1, 2, 3, 4, and 5, respectively. Significance codes follow the R summary: *** *p* < 0.01, ** *p* < 0.05, * *p* < 0.1. Baseline categories are Europe (Origin), Shipping (Background) and 2007 – 2019 (Period).
The geography of authors' affiliation also provides some information on the struc-
ture of the corpus. These results show how different regions are linked to different
research interests. For instance, China and the rest of Asia stand out in particular in the
context of central hubs, while North America shows greater interest in resilience and
transportation systems.

This succinct analysis, using regression based on the prevalence of topics given
by the STM, gives us a first glimpse of the overall structure of the corpus. It shows
the temporal evolution of topics, with the emergence of new interests linked to recent
geopolitical and health crises. The geography of affiliations also yields results that
inform us about differentiated trends from one region of the world to another. Lastly, the
background to each article enables us to observe the links between certain disciplinary
fields and discussing collaborative networks in shipping research, and then, giving
some insight into the determinants and dynamics that structure the field, we will now
analyse in detail the five topics corresponding to the selected partition.

4. Detailed Discussion of the Topics

4.1. Liner Shipping Connectivity and Trade

Liner shipping networks are critical in the design and efficiency of global supply chains (Ducruet and Notteboom 2012). Studying the evolution of their structure helps to understand the major changes that affected international trade in the last few decades: containerisation, emergence of Asian hubs, and various shocks such as armed conflicts and pandemics. Network analysis, focusing on the interaction of the nodes through the edges, is particularly well suited to understand the structure and dynamics of shipping activities. The creation of new ports and lines to cope with demand greatly modified the local and global structures of the shipping networks.

This topic is related to the connectivity of shipping networks and its impact on
international trade and regional economic development. The keywords with the high-
est probability of occurrence refer to maritime connectivity ('connect', 'liner', 'flow')
and economic matters ('trade', 'develop', 'econom'). In addition, FREX reinforces this
interpretation, with words such as 'bilateral' or 'freight' combining it with regional case
studies ('north', 'vietnam', 'america'). Shipping networks are generally approached from
a static point of view, and the dynamics of networks are essentially analyzed through
the evolution of structural metrics such as betweenness and transitivity<fnref n="21" />.

Since the end of the 2010s, several contributions have addressed the gravity aspect of shipping networks<fnref n="22" />. Fugazza and Hoffmann (2017) constructed the Liner Shipping Bilateral Connectivity Index (LSBCI), a measure that brings together maritime (network) connectivity and international trade. The LSBCI includes a network framework<fnref n="23" /> and ‘industry’ metrics like the level of competition on services that connect country pairs and the size of the largest ship on the weakest route. They combine the LSBCI with a standard gravity model to analyze the association between maritime connectivity and bilateral exports of containerizable goods. Fugazza and Hoffmann (2017) showed that improving maritime connectivity between partners significantly affects bilateral exports. For example, two countries sharing an additional common direct destination are associated with about 5% higher value of bilateral exports (see also Kosowska-Stamirowska, 2020)<fnref n="24" />. Following Fugazza and Hoffmann (2017), Del Rosal (2024) estimated the impact of the LSBCI on bilateral exports for 22 classes of manufactured goods (ISIC classification). Thanks to a rich set of estimations, he explores in detail the heterogeneous effects between trade routes and macroregions and shows in particular that the effect of good connectivity is highly significant on long-distance routes.

Although Fugazza and Hoffmann (2017) and Del Rosal (2024) use the value of highly containerisable bilateral exports as a dependent variable, Ducruet, Itoh, and Berli (2020) directly exploit ship flows at the port city level<fnref n="25" />. They aggregated more than two million vessel movements between 1977 and 2016 at the city level and then ran a gravity model on space-L and space-P network topologies<fnref n="26" />. Their results confirmed the positive

<footnote n="21">²¹???</footnote>

<footnote n="22">²²Without explicitly mentioning ‘shipping networks’, Jacks and Pendakur (2010) studied the ‘gravita-
tional’ impact of technological progress on shipping trade. More recently, Jacks, Meissner, and Wolf (2024)
discussed the impact of the opening of the Suez Canal on global trade using a structural gravity model,
thus approaching this research question with a ‘network-like’ approach. Those papers are not included in
our corpus but are of particular interest to understand the structural dynamics of international (shipping)
trade.</footnote>

<footnote n="23">²³It accounts for the number of transshipments required to get from *j* to *k* and the number of common direct connections for example.</footnote>

<footnote n="24">²⁴It is directly connected to the concept of graph ‘communities’ discussed in section 4.4.</footnote>

<footnote n="25">²⁵Another interesting example of the combination - albeit without direct reference to shipping network literature - of Lloyd's data and gravity models is Useche (2023)</footnote>

<footnote n="26">²⁶Building on the idea of Sen et al. (2003), Hu and Zhu (2009) proposed two topologies for the global shipping network: space-L and space-P. In the space-L topology, two ports are connected if and only if there is a *direct* vessel connection between them. The space-P topology is less restrictive and considers that two ports are connected if they are on the same line. Let us take the example of the pendulum route such as Hamburg – Shanghai: in the space-L topology, those two ports are not considered as ‘connected’</footnote>
influence of city size (population) on the intensity of maritime trade and the growing
friction of distance on such flows.

Ducruet and Yoon (2024) looked at the relationship between maritime connectivity
and economic development in North Korea from 1977 to 2021. Notably, they show how
the collapse of the USSR and the Eastern Block in the 1990s provoked a dramatic loss
in maritime connectivity and isolated North Korea from the rest of the world, China
remaining the only exception. While Ducruet and Yoon (2024) studied one of the most
closed countries in the world, Trent et al. (2024) focused on New Zealand, a ‘connectivity
taker’ because of its size and geographical position, namely a small country with no real
market power, relying almost entirely on foreign shipping companies. The ‘proximal
market network’ is composed of ports through which New Zealand connects the rest of
the world, such as Australian hubs, highlighting the risk of becoming peripheral (see
Ducruet 2008 about the hub dependence concept and model). The regional development
of maritime networks also goes hand in hand with questions of regional integration.
Mohamed-Chérif and Ducruet (2016) observed that the multiplication of links across
the Maghreb was better explained by traffic polarization around Tangier-Med than
regional integration, which remains politically and economically limited. Calatayud et
al. (2017a, 2017b) proposed a multilevel analysis of shipping networks at port level and
trade networks at country level.

## 4.2. Hubs, Port Centrality and Competition

This theme includes important keywords which form a coherent whole, of which hub,
centrality, degree, container, development, and competition. Overall, it corresponds to
the comparison of ports using rankings of various centrality measures, to assess their
function in the network and their competitiveness. The theme has a strong affinity with
Asia, where ports compete for transshipment, and which includes the largest ports of
the world.

The early works of physicists focused on the statistical distribution of centrality mea-
sures, to summarize the topology of the network and prove the existence of hubs. For
instance, Xu et al. (2007) and Cui (2014) applied conventional complex network methods,
plotting the log-log distribution of degree centrality<sup>27</sup> and betweenness centrality<sup>28</sup>,
concluding to a scale-free behaviour, as in the seminal work of Barabási and Albert

<footnote>while in space-P they are, because they constitute two elements of the same shipping line.</footnote>

<footnote n="27"><sup>27</sup>Number of links connecting topologically adjacent nodes.</footnote>

<footnote n="28"><sup>28</sup>Number of occurrences of a node on all shortest paths in the network.</footnote>
(1999)<fnref n="29" />. Such an analysis was repeated in many subsequent works, but without always reaching satisfactory results, as the exponent of the power-law line in the plot often scored below the expected threshold.

Soon after, other physicists demonstrated that port centrality was significantly and positively correlated with port throughput in TEUs<fnref n="30" /> (Deng et al., 2009; Hu and Zhu, 2009). Such a correlation between centrality and size has long been observed in the case of urban networks of all kinds (see Li and Neal 2024). Degree centrality in the space-P topology better illustrates the trade relationships of ports, especially gateway ports, as it includes their long-distance connections, which are ignored in space-L. Shenzhen in China, a very large port by its throughput, is overshadowed by the Hong Kong hub in space-L, while in space-P it deploys numerous indirect connections with ports on all continents (Ducruet et al., 2010). Singapore is more central in the space-L network due to its hub function, but it is less central in the space-P network than Shanghai, which is more a gateway port (Duan et al., 2024).

The great majority of related works described port rankings based on various central-ity measures, based on pure network topology, often concluding that the most central ports locate in East Asia, Western Europe, and North America (Liu et al., 2018a, 2018b). Certain works, however, proposed alternative metrics or pushed further the comparison between centrality and throughput. Bartholdi et al. (2016) adapted the UNCTAD's Liner Shipping Connectivity Index (LSCI) at the port level to create the Container Port Con-nectivity Index (CPCI), a composite indicator that is more in line with trade. The CPCI was decomposed by Jarumaneeroj et al. (2023) to study the impact of Panama Canal expansion and of the Hanjin bankruptcy on global port connectivity. Other scholars used the eigenvector accessibility to compare ports, as well as closeness centrality<fnref n="31" />. Zhao et al. (2014) proposed a joint analysis of degree, betweenness, closeness, and eigenvector centralities to differentiate global, regional, and sub-regional hub ports (see also Zheng and Shao, 2022).

Most of the time port centrality is envisaged in a static way. Ducruet and Notteboom (2012) analyzed the evolution of degree and betweenness between 1996 and 2006, high-lighting the decline of Atlantic ports and the rise of Asia-Pacific ports, although container

<footnote n="29">²⁹The “scale-freeness” of a network is a topological property shared by many complex systems in biology, transport, information technology and so on. A network is considered scale-free when the distribution of vertex degrees follows a power law. This relationship is usually written as $P(k) \sim k^{-\gamma}$ with $\gamma$ between 2 and 3.</footnote>

<footnote n="30">³⁰Twenty-Foot Equivalent Units, the standard measure of the volume of container handling at ports.</footnote>

<footnote n="31">³¹Also known as the *Shimbel index*, closeness centrality is the inverse of the number of links between a node and its farthest node in the network.</footnote>
throughput grew everywhere in the same period. The work of Kang and Woo (2017) is a rare example of research looking at the impact of centrality on throughput based on panel data. Their analysis of 20 global ports between 2006 and 2011 concluded that eigen-vector centrality positively affects throughput, like berth length, while betweenness had no apparent effect. Conversely, Bai et al. (2022) analyzed how throughput determines connectivity, using graph theory and principal component analysis. They identified Antwerp and Tanjung Pelepas as outliers due to their superior regional accessibility compared with their throughput.

Another approach has been to compare the centrality of ports at different network scales. Fraser et al. (2016) for instance highlighted the peripherality of South African ports in the global container shipping network despite their hub position regionally. Liu et al. (2022) observed that Piraeus (Greece) ranks high in the Europe-China network by its closeness centrality but remains under Northern Range ports in the intra-European network by its degree and betweenness. The multilayer dimension of the global maritime network, decomposed by main types of vessels (container, general cargo, solid bulk, liquid bulk, passengers and vehicles) was first explored by Ducruet (2013), showing a striking increase of all centrality measures with the number of layers (see also Ducruet, 2017a, 2022; Alderson et al., 2020; Peng et al., 2023). Some scholars considered the multi-level structure of the maritime network, looking at the connectivity of ports at different levels of community structures (Ducruet et al., 2010b), including port-level and country-level linkages (Calatayud et al., 2017a, 2017b), disaggregating the network by shipping companies (Xu et al., 2024e), or coined the network multi-scale for their analysis (Kojaku et al., 2019; Liu et al., 2024b). Parent works looked at the centrality of ports in the hypergraph, a network where one single link (edge) can connect multiple nodes (vertices) (Tocchi et al., 2022; Tinessa et al., 2022; Yu et al., 2022; Li et al., 2024).

**4.3. Vulnerability and robustness to disruptions**

This subfield of shipping network research is one of the most developed in the literature, comprising no less than one-third of the whole corpus. It is very diverse internally, from topological assessments of vulnerability, reaction to economic crises, wars and geopolitical conflicts, as well as natural disasters, the Covid-19 pandemic, main channel disruptions, and targeted attacks.

The topological approach wishes to determine whether a given maritime network is easy to collapse under a variety of attacks on nodes or on links. In the case of links, the level of hub dependence corresponds to the share of the largest flow link in total
port traffic (Ducruet, 2008). The higher the share, the more a port is vulnerable if the link is removed or perturbated (cf. Shenzhen and Hong Kong). Another study analyzed targeted link disruption, concluding that removing links of highest betweenness resulted in a reduction of transshipment and dynamic re-routing potential amongst the busiest port regions (Viljoen and Joubert, 2016). Removing partially or completely canal-dependent traffic flow links (Suez, Panama) from the global liner shipping network allowed concluding that European ports are the most vulnerable to canal disruptions, in terms of betweenness loss (Ducruet, 2016).

At the network level, most analyses simulate attacks on nodes through successive iterations to verify which strategies are more efficient to make the network collapse rapidly. Simulations evaluated the vulnerability of the maritime network under theoretical disruptions (Earnest et al., 2012; Achurra-Gonzalez et al., 2019a, 2019b). Several studies proposed a “resilience assessment framework” with some variants, such as clique percolation, network disintegration, and knock-on effects (Bai et al., 2024), the propagation of cascading failures based on betweenness and traffic (Cao et al., 2025; Ding et al., 2024), and the Copeland method (Dui et al., 2021). Many other simulations at the network level were provided, often concluding that the disruption of large ports is detrimental (Yang and Liu, 2022; Liu et al., 2023b; Jiang et al., 2024; Liu et al., 2024d; Lu et al., 2024) as well as main channels or chokepoints such as Malacca, Suez, and Panama (Wu et al., 2019). Other studies envisaged either irreplaceability (Guo et al., 2024) or substitutability between adjacent ports in the advent of targeted attacks, in the latter case making the network more robust (Wang et al., 2016; Yang and Sun, 2024). The global container shipping network was studied from the perspective of port congestion propagation (Xu et al., 2022b), maritime traffic redistribution to other routes and ports to improve network resilience (Xu et al., 2024a; Xu et al., 2024d), concluding that targeted attacks on betweenness are more efficient than random attacks (He et al., 2022; Liu et al., 2018c; Xia et al., 2024).

One important particularity of ports and shipping networks is their vulnerability to natural disasters. Existing studies have looked at the impact of tsunamis and rising sea levels (Chua et al., 2024), cyclones (Chen et al., 2019; Huang et al., 2024; Poo et al., 2024; Xu et al., 2024c), typhoons (Mou et al., 2024; Wang et al., 2024ca), extreme weather (Poo and Yang, 2024), hurricanes and earthquakes (Rousset and Ducruet, 2020), and natural hazards (Verschuur et al., 2022). However, few of the mentioned studies provided an analysis of a real-world event, compared with those using prediction and simulation tools. Rousset and Ducruet (2020) compared Kobe's Hanshin Earthquake (1995), New
Orleans' Hurricane Katrina (2004), and the Twin Towers attack in New York (2001). In all cases, the density<fnref n="32" /> of the network increased after the event, with container traffic being the most affected, and a common pattern of traffic re-routing was observed at the periphery of the respective port systems. Another example is Huang et al. (2024) who analyzed the effects of Typhoon Ma-on on the container shipping network using over 776 million Automatic Identification System (AIS) trajectory signals, with the goal to enhance the effectiveness of disaster prevention and mitigation policies.

Although Mou et al. (2024) only looked at the probability of port failure under typhoon risk, they also discussed how to safeguard crucial ports, find alternative ports, optimize shipping routes, and improve the reliability of maritime networks. Poo and Yang (2024) measured the maritime transportation system's recent and future climate risks by integrating climate risk indicators, centrality assessment, and a shipping routing model for a sample of 136 port cities worldwide. Poo et al. (2024) analyzed how five major Chinese ports may resist to tropical cyclones, in order to strengthen infrastructure, improve emergency responses, and adopt climate-resilient policies to make them more sustainable and resilient to climate threats. Wang et al. (2024a) established a centrality ranking of Chinese ports in the global shipping network to estimate their robustness to typhoon risks, concluding that performance loss caused by multiple ports is higher than that of a single port. Shen et al. (2019) proposed a theoretical framework based on which they removed the ports most exposed to tropical cyclones and observed the effects of this removal on the structure of the Northwest Pacific and northern Indian networks. Another theoretical framework was provided by Verschuur et al. (2022b) to enhance system-wide resilience, alongside port adaptation, to prevent exacerbation of supply chain losses. Xu et al. (2024c) established a framework considering both direct and indirect impacts of tropical cyclones in the Northwest Pacific, from the perspective of path redundancy.

Contrary to natural disaster studies where empirical investigations are relatively scarce, analyses of the covid-19 impacts on ports and shipping networks have mostly been data-driven. Another difference with natural disasters is that the covid-19 had a global magnitude. Dirzka and Acciaro (2022) used data on liner shipping schedule cancellations as distress signals of the pandemic's impact on global supply chains. While network disruptions first clustered in Asia before rippling along main trade routes, the agility of shipping companies prevented the collapse of the global maritime transport network. Some studies provided convergent results, such as the robustness of large

<footnote n="32">³²Network density refers to the share of actual links in the total maximum number of links.</footnote>
ports (throughput) and the vulnerability of hub ports (betweenness, degree) (Guerrero et al., 2022; Ferrari et al., 2022; Li et al., 2024e; Nguyen and Kim, 2024). Jin et al. (2022) analyzed 55 mainland Chinese ports during January-September 2019 and 2020 based on AIS data, with different outcomes depending on main routes and to the role of external hubs. The study by Pan et al. (2022) used graph theory and a gravity model to add new links and strengthen the container shipping network connectivity. Wu et al. (2024a) concluded to an improved tolerance of the global liner shipping network to deliberate attacks, notwithstanding a decline in port efficiency. Yue et al. (2024a) used a disruption risk propagation model to uncover the hub-and-spokes structure of the global container shipping network. Zhou et al. (2024b) applied a microscopic simulation model to the global port network using AIS data, concluding that the effect of quarantine policies were both direct (port skipping) and indirect (network interaction). Arpin et al. (2024) used network analysis and route dynamic assessment in two different scenarios to show the moderate adaptability of the global liner shipping network to substantial disruptions. Some other works focused more on cruise shipping networks (Kanrak et al., 2022; Li, 2024; Poo et al., 2024), since passenger shipping had been among the most impacted sectors during the pandemic (see Verschuur et al., 2022a).

Lastly, another set of studies focused on the local and global impacts of real-world
shocks, such as the Red Sea crisis (Yap and Yang, 2024; Wang et al., 2024), the Russia-
Ukraine conflict (Xiao et al., 2024; Cong et al., 2024; Polo Martin et al. 2024; Zhang et al.,
2024; Ke et al., 2024; Xiao et al., 2025), and the Suez Canal blockage (Saito et al., 2022; Wan
et al., 2023; Tran et al., 2025). Fang et al. (2018) used a spatiotemporal analytic framework
to measure the impact of various disruptions in South Asia, including military conflict,
lifted economic sanctions, and government elections. Some works looked at the impact
of economic crises. For instance, Mesa-Arango et al. (2019) considered the 1997 Asian
financial crisis and the great recession from 2007 to 2009, based on a multicommodity
bilateral network among countries. Pais Montes et al. (2012) compared the degree
centrality of global ports across three periods (2008-2009, 2009-2010, 2010-2011) to reveal
the impact of the global financial crisis and the pattern of recovery, with a divergence
between throughput evolution and centrality evolution (see also Gonzalez-Laxe et al.,
2012; Freire Seoane et al., 2013). Ducruet et al. (2023) provided a detailed analysis of the
global financial crisis, showing that New York, the East Coast of North America, and
the Caribbean suffered the highest loss of betweenness centrality in 2009. The longer-
term North Korean crisis was documented with precision using vessel movement data
available since the 1970s (Ducruet et al., 2009; Ducruet and Yoon, 2024). Last but not
least, some works considered other sectors than container shipping, looking at the
vulnerability of the LNG maritime supply chain network (Mei et al., 2024), the crude oil
maritime network (Mou et al., 2020), the tanker and dry bulk maritime networks (Xie,
2019), and the global agricultural trade network (Wei et al., 2024; Wei et al., 2025).

**4.4. Communities and spatial structure**

The spatial nature of networks has been the subject of much research in theoretical
and applied network science in recent years (Barthélemy 2011, 2022)<fnref n="33" />. Understanding
the evolution of transport networks cannot be achieved without considering that they
are grounded in space. Planar (roads, rails) and non-planar (airlines, shipping lines)
transport networks are designed according to spatial constraints. In the case of shipping,
the distance between ports is determinant but not sufficient to explain the structure of
the network; Pascali (2017) showed that trade distances between countries are highly
dependent on maritime technology: steamships reduced shipping costs and time and
allowed sailors to navigate without being subject - or to a lesser extent - to the winds.
Containerization, by promoting hub-and-spoke network systems, also had a major effect
on the spatial properties of the maritime network. The structure of shipping networks
is not only explained by physical constraints<fnref n="34" />, but also by meteorological phenomena,
technological advances and organizational changes, which affect the definition of the
space in which ships operate (Ducruet and Notteboom, 2012; Ghorbani et al., 2022;
Ducruet and Itoh, 2022; Xu et al., 2024).

Transport networks – like many other complex systems – are characterized by a
phenomenon of preferential attachment (Newman 2001; Vázquez 2003). It implies that
some nodes – e.g. ports – are very likely to connect with each other when in proxim-
ity and to connect to the same large nodes. This process creates communities in the
network and a whole field researched the best methods to identify these communities
(Newman and Girvan 2004; Fortunato 2010; Fortunato and Newman 2022). In the case of
maritime networks, this property implies that ports are integrated into ‘trade commu-

<footnote n="33"><sup>33</sup>As emphasized in section 4.4 and recalled by Barthélemy (2011) the ‘spatiality’ of networks was already an object of study of quantitative geographers in the 1960s. However, we consider ‘networks’ in the modern way, following the major conceptual and technical progresses of the 1990s (Watts and Strogatz 1998; Barabási and Albert 1999).</footnote>

<footnote n="34">³⁴It should be noted, however, that this remains an important parameter in the structure of shipping networks. Particularly in the case of containerisation, the size of ships and port facilities have a strong impact on the network (see Brooks, Gendron-Carrier, and Rua (2021), Ducruet et al. (2024) on the impact of port depth and Malchow (2017) and Ge et al. (2021) on operational challenges related to the size of containerships.</footnote>
nities'; these clusters are explained by various factors such as geography, history, trade agreements, and port functions. The geographical rationale of maritime communities is highly related to distance: neighboring ports are more likely to connect, especially when keeping smaller ships in the analysis. Industry factors, such as the emergence of transshipment ports and the containerization of cargo, greatly impacted the structure of shipping networks (Rodrigue and Notteboom 2013; Guerrero and Rodrigue 2014; Cosar and Demir 2018; Tsiotas and Ducruet 2021).

Kaluza et al. (2010) and Sun et al. (2012) were the first to explore community structures in shipping networks using clustering algorithms. Kaluza et al. (2010) built three global shipping networks, for container ships, dry bulk carriers, and oil tankers. Using a modularity-based approach (Leicht and Newman 2008) they found communities that correspond mainly to geographical entities, with a few that are more dispersed. The communities are very large, with a regional dimension, whereas more functional ones did not emerge. This result exemplifies an important limit of the application of modularity-based algorithms to networks, namely the ‘resolution limit’ (Fortunato and Barthélemy 2007). Modularity-based algorithms have been shown to fail in detecting small, specific communities. When analyzing shipping networks, this issue implies that we observe mainly geographical modules and fail to uncover the functional dimension of trading communities<fnref n="35" />.

The variety of strategies pursued by shipping companies and the differences in trading communities between companies are explored by Xu et al. (2024) within the framework of a multiplex network. Certain hubs form communities that are very specific to certain shipping companies, and disappear when several ones are considered. The authors highlight the critical role of certain ports for specific players, while other ports have a central position for the Global Container Shipping Network (GCSN) as a whole. The ‘multi-criticality’ of the different port infrastructures and the competition between them is thus approached at different levels of aggregation in the shipping industry.

Thus, efforts to apply clustering models should be directed towards a better understanding of the temporal and functional dynamics of maritime networks. Now that the spatial dimension is well understood, the exploration of new methods such as dynamic stochastic block models (DSBM) should be pursued (Matias and Miele 2017).

<footnote n="35">³⁵Geographical communities are indeed existing in shipping networks, and clustering algorithms are useful in delineating them</footnote>
**5. Discussion: Missing Links?**

This study reviewed the research trends of shipping network research since the original application of graph theory to a maritime network by Robinson (1968). Four main themes could be identified within the corpus of articles published between 2007 and 2025: trade, centrality, vulnerability, and spatial structure, which comprise most of the articles. However, certain subfields remain strikingly small despite their core importance in port and maritime studies in general.

One of these subfields relates to environmental impacts. Some studies did not use network analysis or did not measure pollution. For example, Li et al. (2024) studied the South China Sea shipping network without measuring carbon emissions, while Chen et al. (2024) and Zhou et al. (2024a) did not use network analysis. Another example is Karountzos et al. (2023)<fnref n="36" /> who used a Geographical Information System (GIS) to examine the possible introduction of electric ferries in the Greek shipping network. Some studies went one step further, such as Tran and Lam (2024) analyzing the global network of CMA-CGM to reveal that in-port emissions concentrate in a small number of key hubs. Guo et al. (2023) concluded that container throughput, liner shipping connectivity, and trade openness have a positive impact on marine greenhouse gas (GHG) emissions at the country level. Some other studies used the complex network framework. Yue et al. (2024) estimated the carbon footprint of the Suez Canal disruption along the Europe-Asia route, while Kanrak et al. (2023) concluded that the best connected Caribbean ports tend to locate outside Emission Control Areas because the regulations are barriers for cruise ships entering the ports. Marine bioinvasions attracted peripheral interest from a network perspective, although they have enormous environmental, economic, cultural, and human health impacts. Kaluza et al. (2010) and Kölzsch and Blasius (2011) provided the first analyses of the kind using a complex network framework. They particularly used betweenness centrality and community detection to estimate how invasive species can travel through the global shipping network, carried by ballast water. It is only much later that other studies were conducted, to measure the influence of centrality on the spread of non-indigenous species (NIS) between ports (Lenzen et al., 2023; Bereza et al., 2023; Song et al., 2024; Shi et al., 2024), with diverse outcomes.

Another striking deficiency of shipping network research is the subtheme of local socio-economic development. This contrasts sharply with the abundant literature on port-city relationships and port economic impacts. Very few works put port centrality

<footnote n="36">³⁶See also Karountzos et al. (2024) and Karountzos, Giannaki, and Kepaptsoglou (2024)</footnote>
in relation with the socio-economic attributes of nodes. Wan et al. (2021) included port
city GDP and throughput in their classification of container ports along the Maritime
Silk Road. Kanrak and Nguyen (2022) are one of the rare studies to consider node
attributes in their analysis of the Asian-Australasian shipping network (location, number
of attractions and visitors, rating score, popularity, and population). Guo and Qin (2022)
studied the port-urban network system in China by combining cargo flows with people,
capital, technology, and information flows to model the relationship between the coastal
port system and its urban network. An original approach has been to measure the
combined centrality of ports in road, railway, and maritime networks simultaneously
(Berli et al., 2018; Indriastiwi and Hadiwardoyo, 2021; Jung and Thill, 2022; Polo Martin
and Ducruet, 2024; Da Costa et al., 2025), as well as air-sea connectivity (Parshani et al.,
2011). This necessitated aggregating ports, stations, and airports at the level of urban
agglomerations, often concluding that combined centrality is better correlated with
city size than single centrality.<fnref n="37" /> Despite these advances, we still lack of an analysis
that would address the issue of endogeneity, as maritime connectivity and local socio-
economic development occur through reverse causation.

Last but not least, we found only a few works on the influence of shipping alliances
on maritime networks. Wilmsmeier and Notteboom (2011) soon discussed the effects of
shipping lines' strategies and the role of alliances on the connectivity between Europe
and South America. Yap and Zahraei (2018) look at how major routes such as Europe-
Asia have been reshuffled due to the rationalization of individual shipping lines and
the growth of alliances. Liu et al. (2024b) found that the alliance shipping network is
more robust than the one of individual companies. Yamamoto and Takebayashi (2024)
concluded that hub-and-spokes systems are more profitable for carrier alliance. One of
the most advanced approaches is that of Gou et al. (2024), offering a multilevel analysis
of the global liner network with two layers (shipping lines and alliances), concluding that
alliances ease competition, while making the network more efficient, denser, and stable,
supporting shipping network integration. Finally, Wang et al. (2020) analyzed the COSCO
and CSCL shipping networks before and after their merger. Complementarities were
found before the merger, but afterward the merger also had the effect of strengthening
the key hub ports of the network.

In terms of methods, since the review by Kanrak, Nguyen, and Du (2019) of the
analytical methods applied to shipping networks, many network models have in fact

<footnote n="37">³⁷Two works outside of the corpus are worthy of investigation: Ducruet and Itoh (2015) and Ducruet et al. (2017) analyzed the centrality of subnational regions in combination with various socio-economic indicators, such as population density, unemployment rate, GDP per capita, and main economic sectors</footnote>
been neglected if not ignored in the literature on shipping networks. The random graph
or Erdős and Rényi model combines graph theory and probability theory, function-
ing as a benchmark to study the topology and connectivity of a network (Kanrak and
Nguyen, 2022a). The exponential random graph model (ERGM) is a statistical model of
social networks that explains the formation of links, based on the assumption that the
emergence of a relationship could be influenced by other relationships or individual
characteristics (Kanrak and Nguyen, 2022b). However, no study has been done using this
model which is rather surprising, given that shipping networks witness a highly hierar-
chical structure that echoes the processes of rich-club (most central nodes connecting
each other, or assortativity) and preferential attachment (new nodes and links primarily
connected high-degree nodes, in a scale-free network). The vast majority of studies used
descriptive statistics instead, such as local measures of centrality (degree, clustering
coefficient), global accessibility measures (betweenness, closeness, eigenvector), and
network-level indices to summarize the structure of the network (diameter, average
shortest path length, density, degree distribution), but without a modeling approach,
despite its wide use in other domains. In terms of network dynamics, although fine-
grained shipping data is increasingly available, very few works have proposed a detailed
temporal approach of centrality, except for Guinand and Pigné (2015) looking at daily,
weekly, and monthly centrality in the global container shipping network. It should not
be forgotten that preferential attachment in a scale-free network is a process rather
than a static property.

**Conclusion**

Without a doubt, shipping network research is a buoyant field today, given the rise of
publications and the diversity of approaches. Like in many other fields and across all
scientific disciplines, port and maritime research experienced a massive integration
of the complex network framework in the 2000s, offering new ways to compare ports
and understand maritime transport. However, important issues related to globalization
and regional integration processes, international trade patterns (e.g. gravity), and local
socio-economic development remain poorly discussed in the literature on shipping
networks. The exact meaning of port centrality is rarely discussed as well, although it
provides in some cases interesting deviations from the more classic ranking of ports
according to throughput tonnage.

Moreover, focusing on the only shipping network tended to ignore the fact that
ports and maritime transport are only one component of the overall supply and value chain (Robinson 2002). The lack of data on the hinterland remains a major obstacle, forcing scholars to focus on the only road or rail infrastructure in combination with the shipping network. This explains the drastic absence of empirical studies on the vertical integration of maritime networks.

Further research shall lean towards several possible directions. One of them is the
deeper analysis of port performance and competitiveness, where port centrality should
be included as one factor among others, to test its relative influence, and to elucidate
the respective roles of various types of centrality at different scales. Another avenue
of research is on environmental impacts, including public health issues and pollution,
to verify whether particular network structures may reinforce or lower such impacts,
also considering the technical characteristics of vessels and their navigation patterns.
Economic development in general is a promising theme, should it be about cities,
regions, or countries, given its very close relationship with trade and maritime transport.
Lastly, massive extraction of historical shipping data may allow fine-grained analyses of
past events and their influence on shipping network structures. The discovery of Lloyd's
List archives on daily vessel movements between ports of the world undoubtedly offered
new possibilities to track multiple shocks that affected ports and shipping networks
back in the 19th century.

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# Appendix A. Co-authorship Networks

<figure><img src="image_6.png" /><figcaption>FIGURE A1. Co-authorship network (2007–2017)</figcaption></figure>

*Notes*: Two authors are connected if they have written an article together.

The configuration of co-authorships is relatively scattered in the first period (Figure A1), mainly due to the low number of articles. Interregional collaborations are near to non-existent, except for the dyad between Cullinane (Europe) and Wang (China). European scholars concentrate the bulk of publications, with Ducruet-Notteboom-Wilmsmeier as the core community.
<figure><img src="image_7.png" /><figcaption>FIGURE A2. Co-authorship network (2018 – 2025)</figcaption></figure>

*Notes*: Two authors are connected if they have written an article together.

The second period (Figure A2) witnesses a fully different pattern, with multiple cores and more interregional connexions. Europe is still in a relatively central position with Ducruet-Notteboom, but China clearly dominates most of the communities. In the core community, Poo and Claramunt (Europe) have emerged through intensive collaborations with Chinese scholars, as well as Kanrak (Oceania), Lau and Ng (Hong Kong), Jarumaneeroj (Indonesia), and Shibasaki (Japan). The most publishing Chinese authors are Xu M., Yang T., Zhang L., Mou N., Peng P., and Lu F., while Wang L., Mei Q., and Yang Y. occupy important bridge roles to make this core community interconnected.
# Appendix B. Additional Estimations

## B.1. A Six-Topics Partition

<table><caption>TABLE A1. Top Words from the STM model with 6 topics</caption><thead><tr><td>Topic</td><td>Highest Prob</td><td>FREX</td><td>Lift</td><td>Score</td></tr></thead><tbody><tr><td>1</td><td>cruis, vessel, oper, ship, rout, arctic, use, emiss, chang, contain</td><td>emiss, cruis, arctic, suez, asr, passag, itinerari, season, voyag, water</td><td>acceler, contact, ice, inventori, murmansk, plant, prompt, summer, tonn, world-wid</td><td>cruis, arctic, itinerari, asr, passag, emiss, season, nis, dual-cor, visit</td></tr><tr><td>2</td><td>connect, trade, region, contain, liner, use, model, transport, econom, effect</td><td>bilater, export, freight, product, period, connect, variabl, partner, demand, africa</td><td>agenda, best, bilater, calibr, capita, contract, country-level, crise, eight, endogen</td><td>bilater, -go, trade, country', lsbci, capita, partner, output, freight, canari</td></tr><tr><td>3</td><td>contain, central, hub, connect, competit, develop, studi, servic, region, degre</td><td>hong, asian, kong, busan, singapor, competit, central, shanghai, hub, allianc</td><td>fierc, hole, late, latest, penang, qingdao, sub-hub, topsi, alphabet, arrang</td><td>busan, competit, hong, asian, kong, singapor, hacc, tier, alphabet, cosco</td></tr><tr><td>4</td><td>resili, vulner, disrupt, impact, transport, trade, model, studi, assess, attack</td><td>resili, failur, cascad, attack, disrupt, conflict, vulner, robust, critic, risk</td><td>aid, catastroph, confid, conflict, encounter, fail, faster, fragil, hazard, intent</td><td>resili, cascad, failur, attack, disrupt, conflict, glns, reliabl, crude, risk</td></tr><tr><td>5</td><td>structur, spatial, transport, communiti, trade, system, flow, region, traffic, studi</td><td>urban, communiti, citi, spatial, linkag, articl, inter-, function, strateg, coupl</td><td>airport, australia, biparitit, bodi, chosen, clarifi, communic, compil, connector, convent</td><td>communiti, urban, spatial, citi, gmn, updat, inter-, multiplex, socioeconom, linkag</td></tr><tr><td>6</td><td>rout, transport, ship, method, use, data, road, node, model, link</td><td>msr, silk, road, shortest, path, algorithm, advantag, tanker, bulk, edg</td><td>abstract, allevi, billion, blockag, bottleneck, footprint, heavy-tail, hope, msr, neighbourhood</td><td>msr, silk, tanker, advantag, ship, chart, waypoint, oil, bulk, road</td></tr></tbody></table>
<table><caption>TABLE A2. Regression Results for Topics 1-6 (Updated Model)</caption><thead><tr><th rowspan="2">Variable</th><th colspan="6">Topic</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>0.02369</td><td>0.39416***</td><td>0.17719***</td><td>0.01703</td><td>0.28595***</td><td>0.10200*</td></tr><tr><td>2020-2025</td><td>0.11341***</td><td>-0.00270</td><td>-0.06031*</td><td>0.11080**</td><td>-0.09880**</td><td>-0.06255*</td></tr><tr><td>China</td><td>-0.02373</td><td>-0.20694***</td><td>0.18153***</td><td>0.06725</td><td>-0.11060**</td><td>0.09258*</td></tr><tr><td>North America</td><td>-0.02683</td><td>-0.11109</td><td>-0.00152</td><td>0.10650</td><td>-0.04424</td><td>0.07638</td></tr><tr><td>Rest of Asia</td><td>0.15072**</td><td>-0.22830***</td><td>0.31065***</td><td>-0.02693</td><td>-0.17163**</td><td>-0.03592</td></tr><tr><td>Economics</td><td>0.11645</td><td>-0.08334</td><td>-0.02469</td><td>-0.02042</td><td>-0.11176</td><td>0.12096</td></tr><tr><td>Engineering & data sciences</td><td>-0.09225*</td><td>-0.15229*</td><td>-0.16267*</td><td>0.13061*</td><td>-0.01142</td><td>0.28656***</td></tr><tr><td>Environment & energy</td><td>0.10782</td><td>-0.15236*</td><td>-0.20551*</td><td>0.29789**</td><td>-0.01486</td><td>-0.03333</td></tr><tr><td>Geography</td><td>0.01326</td><td>-0.04273</td><td>-0.06036</td><td>-0.00673</td><td>0.12195*</td><td>-0.02461</td></tr><tr><td>Geosciences</td><td>-0.01974</td><td>-0.15374*</td><td>-0.19289*</td><td>0.03740</td><td>0.22512*</td><td>0.10403</td></tr><tr><td>Marine sciences</td><td>0.01766</td><td>-0.11488*</td><td>-0.16735**</td><td>0.09616</td><td>0.01751</td><td>0.15063*</td></tr><tr><td>Multidisciplinary</td><td>0.16326*</td><td>-0.12160*</td><td>-0.14088*</td><td>0.02379</td><td>0.03606</td><td>0.03894</td></tr><tr><td>Networks & complex systems</td><td>0.05010</td><td>-0.00999</td><td>-0.18866*</td><td>-0.04923</td><td>0.14299</td><td>0.05632</td></tr><tr><td>Physics & natural sciences</td><td>0.02751</td><td>-0.15812*</td><td>-0.16115*</td><td>0.03981</td><td>0.06392</td><td>0.18914*</td></tr><tr><td>Transport & logistics</td><td>-0.07604</td><td>-0.00015</td><td>-0.05151</td><td>0.10628*</td><td>-0.00817</td><td>0.03019</td></tr></tbody></table>

Note: Columns (1), (2), (3), (4), (5), and (6) show coefficients for Topics 1, 2, 3, 4, 5, and 6, respectively. Significance codes follow the R summary: *** $p < 0.01$, ** $p < 0.05$, * $p < 0.1$. Baseline categories are Europe (Origin), Shipping (Background) and 2007-2019 (Period).
<figure><img src="image_8.png" /><figcaption>FIGURE A3. Top-10 most probable words in a six-topics partition</figcaption></figure>

The most probable words in a six-topics partition also reveal some interesting results: in particular, it makes it possible to create a new topic focusing on methodological issues (with words such as method, link, data). However, this methodological question is outside the scope of our more thematic analysis. This also reinforces the coherence of an approach with a limited number of topics focusing on the major trends in the field over the years.
## B.2. A Ten-Topics Partition

<table><caption>TABLE A3. Top Words from the STM model with 10 topics</caption><thead><tr><td>Topic</td><td>Highest Prob</td><td>FREX</td><td>Lift</td><td>Score</td></tr></thead><tbody><tr><td>1</td><td>cruis, arctic, connect, studi, rout, ship, oper, chang, line, use</td><td>cruis, arctic, itinerari, asr, passag, season, voyag, core, line, allianc</td><td>acceler, branch, mur-mansk, dual-cor, latitud, medium, one-way, round-trip, cruís, asr</td><td>cruís, itinerari, arctic, asr, dual-cor, passag, season, caribbean, mur-mansk, acceler</td></tr><tr><td>2</td><td>connect, trade, liner, re-gion, servic, effect, use, contain, measur, indic</td><td>bilater, connect, africa, partner, liner, variabl, export, posit, servic, african</td><td>bilater, capita, endow, -go, agenda, brief, coun-try', gross, longitudin, ls-bci</td><td>bilater, -go, lsbci, country', export, liner, african, capita, trade, variabl</td></tr><tr><td>3</td><td>central, hub, contain, competit, connect, studi, develop, region, liner, use</td><td>hong, busan, shanghai, kong, singapor, asian, central, hub, competit, southeast</td><td>hacc, hole, qingdao, sub-hub, topsi, alphabet, be-liev, cyclic, dalian, hub-southeast</td><td>busan, asian, hong, kong, competit, alpha-bet, singapor, hacc, shanghai, sna</td></tr><tr><td>4</td><td>trade, transport, oil, in-tern, countri, use, bulk, data, tanker, flow</td><td>oil, tanker, bulk, energi, trade, crude, lng, price, strait, volum</td><td>add, annual, apart, clean, earthquake, food, mass, modul, repeat, dri</td><td>oil, tanker, bulk, energi, crude, trade, modul, lng, dri, price</td></tr><tr><td>5</td><td>structur, communiti, spatial, transport, citi, system, connect, flow, contain, studi</td><td>communiti, citi, urban, spatial, linkag, inter-, function, articl, hierar- chi, strateg</td><td>communic, gmn, multi-plex, revisit, stake, bi-partit, chosen, geomat, lloyd', non-planar</td><td>communiti, urban, citi, updat, gmn, inter-, hierarchi, socioeconom, linkag, multiplex</td></tr><tr><td>6</td><td>method, road, china, silk, rout, msr, studi, construct, traffic, use</td><td>silk, road, msr, china, along, plan, advantag, belt, method, extract</td><td>waypoint, wmtn, barabási-albert, bri, cambodia, chart, data-driven, digit, match, rich-club</td><td>msr, silk, road, vietnam, wmtn, chart, belt, ad-vantag, coeffici, extract</td></tr><tr><td>7</td><td>resili, vulner, node, dis-rupt, transport, attack, failur, studi, robust, im-pact</td><td>failur, attack, vulner, re-sili, robust, glsn, critic, cascad, disrupt, deliber</td><td>failur, interfer, suscept, well-connect, compens, cyclon, system', trans-miss, typhoon, tropic</td><td>resili, failur, cascad, at-tack, glsn, well-connect, deliber, disrupt, vulner, cyclon</td></tr><tr><td>8</td><td>transport, model, region, use, contain, graph, dif-fer, flow, rout, trade</td><td> databas, graph, freight, limit, methodolog, de-mand, literatur, korean, discuss, occur</td><td>aris, contract, frontier, interfac, understood, complement, gis, hand, immedi, interv</td><td>principl, korean, gis, freight, databas, reliabl, review, hypergraph, frontier, interv</td></tr><tr><td>9</td><td>contain, rout, covid-, im-pact, chang, model, pan-dem, transport, indic, disrupt</td><td>recoveri, covid-, pandem, emiss, cost, decreas, car-rier, carbon, individu, reduct</td><td>black, catastroph, con-sum, green, inventori, manila, profit, tax, -day, accomplish</td><td>covid-, recoveri, accom-plish, emiss, pandem, carbon, black, conflict, profit, cancel</td></tr><tr><td>10</td><td>ship, risk, vessel, model, water, data, invas, use, rout, suppli</td><td>invas, speci, nis, wa-ter, risk, spread, propag, biosecur, ship, delay</td><td>aquat, arriv, bioinvas, high-risk, nis, non-indigen, ballast, biofoul, biosecur, ecoregion</td><td>arriv, nis, invas, wa-ter, biosecur, speci, risk, visit, flag, bioinvas</td></tr></tbody></table>
<table><caption>TABLE A4. Regression Results for Topics 1–10 (Updated Model)</caption><thead><tr><th rowspan="2">Variable</th><th colspan="10">Topic</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>(Intercept)</td><td>0.03288</td><td>0.25676***</td><td>0.14770***</td><td>0.07875*</td><td>0.18857***</td><td>0.03938</td><td>-0.01318</td><td>0.19857***</td><td>0.03936</td><td>0.03218</td></tr><tr><td>2020-2025</td><td>0.05046*</td><td>0.01473</td><td>-0.06434*</td><td>-0.08296**</td><td>-0.03323</td><td>-0.03818</td><td>0.05705*</td><td>-0.03909</td><td>0.09114**</td><td>0.04288*</td></tr><tr><td>China</td><td>0.06935*</td><td>-0.11362**</td><td>0.11693***</td><td>0.07530*</td><td>-0.04862</td><td>0.13673***</td><td>0.07565*</td><td>-0.18707***</td><td>-0.02647</td><td>-0.09733***</td></tr><tr><td>North America</td><td>-0.03892</td><td>-0.15430*</td><td>0.00428</td><td>0.15820*</td><td>0.01136</td><td>0.03668</td><td>0.01383</td><td>-0.05565</td><td>-0.09302</td><td>0.12048</td></tr><tr><td>Rest of Asia</td><td>0.13278**</td><td>-0.07877</td><td>0.20652***</td><td>-0.02493</td><td>-0.14339**</td><td>0.04513</td><td>0.00156</td><td>-0.15523***</td><td>0.03578</td><td>-0.01721</td></tr><tr><td>Economics</td><td>-0.09898</td><td>-0.01721</td><td>0.01132</td><td>-0.01172</td><td>-0.12032</td><td>-0.03815</td><td>0.01910</td><td>0.00546</td><td>-0.03305</td><td>0.28908*</td></tr><tr><td>Engineering & data sciences</td><td>-0.12548*</td><td>-0.12529*</td><td>-0.11549*</td><td>0.06586</td><td>-0.08225</td><td>0.12521*</td><td>0.11660*</td><td>0.08354</td><td>-0.05142</td><td>0.10800*</td></tr><tr><td>Environment & energy</td><td>-0.04831</td><td>-0.15712*</td><td>-0.14239*</td><td>0.24047**</td><td>-0.08485</td><td>-0.08956</td><td>0.09128</td><td>-0.01240</td><td>-0.03409</td><td>0.23584**</td></tr><tr><td>Geography</td><td>-0.01554</td><td>-0.05808</td><td>-0.07075</td><td>0.02349</td><td>0.06615</td><td>-0.04328</td><td>0.00209</td><td>0.07013</td><td>0.04386</td><td>-0.01777</td></tr><tr><td>Geosciences</td><td>-0.08028</td><td>-0.18034*</td><td>-0.14759*</td><td>0.02120</td><td>-0.00932</td><td>0.04946</td><td>0.00001</td><td>0.28463**</td><td>-0.04707</td><td>0.10760</td></tr><tr><td>Marine sciences</td><td>-0.08370</td><td>-0.11155*</td><td>-0.12337**</td><td>0.00916</td><td>-0.04337</td><td>0.07230</td><td>0.10810*</td><td>0.05976</td><td>0.06387</td><td>0.04962</td></tr><tr><td>Multidisciplinary</td><td>0.05377</td><td>-0.17616**</td><td>-0.09436</td><td>0.11541*</td><td>-0.02848</td><td>-0.02276</td><td>0.03031</td><td>-0.01639</td><td>0.02274</td><td>0.11556*</td></tr><tr><td>Networks & complex systems</td><td>0.01574</td><td>-0.06879</td><td>-0.15057*</td><td>0.09134</td><td>0.10128</td><td>0.06417</td><td>-0.00642</td><td>0.02614</td><td>-0.07093</td><td>-0.00116</td></tr><tr><td>Physics & natural sciences</td><td>-0.10406</td><td>-0.14617*</td><td>-0.19286**</td><td>-0.01152</td><td>0.04980</td><td>0.23249**</td><td>0.12330</td><td>0.00069</td><td>-0.03365</td><td>0.07886</td></tr><tr><td>Transport & logistics</td><td>-0.10101*</td><td>0.01392</td><td>-0.05694</td><td>-0.04689</td><td>-0.02800</td><td>0.02074</td><td>0.11750*</td><td>0.06305</td><td>-0.00438</td><td>0.02051</td></tr></tbody></table>

Note: Columns (1), (2), (3), (4), (5), (6), (7), (8), (9), and (10) show coefficients for Topics 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, respectively. Significance codes follow the R summary: *** p < 0.01, ** p < 0.05, * p < 0.1. Baseline categories are Europe (Origin), Shipping (Background) and 2007 - 2019 (Period).
<figure><img src="image_9.png" /><figcaption>FIGURE A4. Top-10 most probable words in a 10 topics partition</figcaption></figure>
**Appendix C. Corpus of Selected Shipping Network Studies, 2007-2025**

1. Achurra-Gonzalez P.E., Angeloudis P., Goldbeck N., Graham D.J., Zavitsas K., Stettler M.E.J. (2019a) Evaluation of port disruption impacts in the global liner shipping network. *Journal of Shipping and Trade*, 4(3): https://doi.org/10.1186/s41072-019-0019-0

2. Achurra-Gonzalez P.E., Novati M., Foulser-Piggott R., Graham D.J., Bowman G., Bell M.G.H., Angeloudis P. (2019b) Modelling the impact of liner shipping network perturbations on container cargo routing: Southeast Asia to Europe application. *Accident Analysis and Prevention*, 123: 399-410.

3. Alderson D.L., Funk D., Gera R. (2020) Analysis of the global maritime transportation system as a layered network. *Journal of Transportation Security*, 13(3): 291-325.

4. Álvarez N.G., Adenso-Díaz B., Calzada-Infante L. (2021) Identifying port maritime communities: application to the Spanish case. *European Transport Research Review*, 13(36): https://doi.org/10.1186/s12544-021-00495-1

5. Arpin W.T., Pan Q., Xia H. (2024) A unique perspective on vulnerability and route dynamics in the global liner shipping network. *Operations Research Forum*, 5: 77.

6. Bai X., Ma Z., Zhou Y. (2023) Data-driven static and dynamic resilience assessment of the global liner shipping network. *Transportation Research Part E*, 170: 103016.

7. Bai X., Cheng L., Yang D., Cai O. (2022) Does the traffic volume of a port determine connectivity? Revisiting port connectivity measures with high-frequency satellite data. *Journal of Transport Geography*, 102: 103385.

8. Bartholdi J.J., Jarumaneeroj P., Ramudhin A. (2016) A new connectivity index for container ports. *Maritime Economics & Logistics*, 18(3): 231-249.

9. Bereza D., Grey E., Shenkar N. (2023) Prioritizing management of high-risk routes and ports by vessel type to improve marine biosecurity efforts. *Journal of Environmental Management*, 336: 117597.

10. Berli J., Bunel M., Ducruet C. (2018) Sea-land interdependence in the global maritime network: the case of Australian port cities. *Networks and Spatial Economics*, 18(3): 467-471.

11. Cai W., Liang F., Wan Y., Zhong H., Gu Y. (2021) An innovative approach for constructing a shipping index based on dynamic weighted complex networks. *Physica A*, 578: 126101.

12. Calatayud A., Mangan J., Palacin R. (2017a) Connectivity to international markets: a multi-layered network approach. *Journal of Transport Geography*, 61: 61-71.

13. Calatayud A., Mangan J., Palacin R. (2017b) Vulnerability of international freight
flows to shipping network disruptions: a multiplex network perspective. *Transportation Research Part E*, 108: 195-208.

14. Cao Y., Xin X., Jarumaneeroj P., Li H., Feng Y., Wang J., Wang X., Pyne R., Yang Z. (2025) Data-driven resilience analysis of the global container shipping network against two cascading failures. *Transportation Research Part E*, 193: 103857.

15. Carlini E., Monteiro de Lira V., Soares A., Etemad M., Brandoli B., Matwin S. (2022) Understanding evolution of maritime networks from automatic identification system data. *GeoInformatica*, 26: 479-503.

16. Chen D., Yang D., Yang Z. (2024a) Port investment along the Maritime Silk Road considering mutual selections of the industries planned for transfer and recipient countries with location advantage. *Maritime Policy and Management*, 51(6): 1229-1251.

17. Chen J., Liu X., Zhou S., Jia K. (2023) Knowledge mapping analysis of resilient shipping network using CiteSpace. *Ocean and Coastal Management*, 244: 106775.

18. Chen K., Su S., Gong Y., Xin X., Zeng Q. (2024b) Coastal transportation system green policy design model based on shipping network design. *International Journal of Logistics Research and Applications*, 27(3): 428-449.

19. Cheng L., Yan Z., Xiao Y., Chen Y., Zhang F., Li M. (2019) Using big data to track marine oil transportation along the 21st-century Maritime Silk Road. *Science China Technological Sciences*, 62: 677-686.

20. Cheung K.F., Bell M.G.H., Pan J.J., Perera S. (2020) An eigenvector centrality analysis of world container shipping network connectivity. *Transportation Research Part E*, 140: 101991.

21. Chua C.T., Otake T., Li T., Cheng A.C., Qiu Q., Li L., Suppasri A., Imamura F., Switzer A.D. (2024) An approach to assessing tsunami risk to the global port network under rising sea levels. *Npj Natural Hazards*, 1: 38.

22. Cong L., Zhang H., Wang P., Chu C., Wang J. (2024) Impact of the Russia-Ukraine conflict on global marine network based on massive vessel trajectories. *Remote Sensing*, 16(8): 1329.

23. Cui D. (2014) The complex properties of Chinese ship-transport networks. *Applied Mechanics and Materials*, 556: 6483-6486.

24. Cullinane K.P.B., Wang Y. (2012) The hierarchical configuration of the container port industry: an application of multiple linkage analysis. *Maritime Policy and Management*, 39(2): 169-187.

25. da Costa G.A.A., Mendes A.B., da Cruz J.P.G. (2025) Brazilian maritime containerized cabotage competitiveness assessment based on a multimodal super network.
*Journal of Transport Geography*, 122: 104062.

26. de Bruin G.J., Barata A.P., van den Herik H.J., Takes F.W., Veenman C.J. (2022) Fair automated assessment of noncompliance in cargo ship networks. *EPJ Data Science*, 11(13): https://doi.org/10.1140/epjds/s13688-022-00326-w

27. Del Mondo G., Peng P., Gensel J., Claramunt C., Lu F. (2021) Leveraging spatio-temporal graphs and knowledge graphs: Perspectives in the field of maritime transportation. *ISPRS International Journal of Geo-Information*, 10(8): 541.

28. Del Rosal I., Moura T.G.Z. (2022) The effect of shipping connectivity on seaborne containerised export flows. *Transport Policy*, 118: 143-151.

29. Del Rosal I. (2024) Trade effects of liner shipping across world regions. *Maritime Business Review*, 9(1): 2-16.

30. Deng P., Song L., Xiao R., Huang C. (2022) Evaluation of logistics and port connectivity in the Yangtze River Economic Belt of China. *Transport Policy*, 126: 249-267.

31. Deng W.B., Guo L., Li W., Cai X. (2009) Worldwide marine transportation network: efficiency and container throughput. *Chinese Physics Letters*, 26(11): 118901.

32. Dial R., de Oliveira G., Schaffar A. (2021) The spatial dimension of maritime connectivity: The case of Northern Europe and the Mediterranean. *Canadian Journal of Regional Science*, 44(1): 35-45.

33. Ding C., Li Z. (2022) Research on the shipping network structure under the influence of Arctic routes. *GeoJournal*, 87: 1027-1045.

34. Ding C., Li Z., Chen X., Shi X. (2022) The evolution and competition patterns of shipping networks under the influence of an Arctic route system. *Maritime Policy and Management*, 49(4): 510-527.

35. Ding C., Wang Z., Xu S. (2024) Study on the cascading failure robustness of the Belt and Road land-sea transport network under emergencies. *Symmetry*, 16(6): 736.

36. Dirzka C., Acciaro M. (2022) Global shipping network dynamics during the COVID-19 pandemic's initial phases. *Journal of Transport Geography*, 99: 103265.

37. Doshi D., Malhotra B., Bressan S., Lam J.S.L. (2012) Mining maritime schedules for analyzing global shipping networks. *International Journal of Business Intelligence and Data Mining*, 7(3): 186-202.

38. Duan W., Li Z., Zhou Y., Deng Z. (2024) A novel technical framework for the evaluation of node significance and edge connectivity in global shipping network. *Journal of Marine Science Engineering*, 12(8): 1239.

39. Ducruet C. (2008) Hub dependence in constrained economies: the case of North Korea. *Maritime Policy and Management*, 35(4): 374-388.
40. Ducruet C. (2013) Network diversity and maritime flows. *Journal of Transport Geography*, 30: 77-88.

41. Ducruet C. (2016) The polarization of global container flows by interoceanic canals: geographic coverage and network vulnerability. *Maritime Policy and Management*, 43(2): 242-260.

42. Ducruet C. (2017) Multilayer dynamics of complex spatial networks: the case of global maritime flows (1977-2008). *Journal of Transport Geography*, 60: 47-58.

43. Ducruet C. (2021) Cities, diversity, and global maritime networks. *KMI International Journal of Maritime Affairs and Fisheries*, 13(2): 35-51.

44. Ducruet C. (2022) Port specialization and connectivity in the global maritime network. *Maritime Policy and Management*, 49(1): 1-17.

45. Ducruet C., Cocuzza E., Ignaccolo M. (2023) Analysis of the maritime networks: criticalities and resilience. *Transportation Research Procedia*, 69: 878-885.

46. Ducruet C., Yoon I.J. (2024) Maritime trade and economic development in North Korea. *Journal of Transport Geography*, 120: 103990.

47. Ducruet C., Cuyala S., El Hosni A. (2016) The changing influence of city-systems on global shipping networks: an empirical analysis. *Journal of Shipping and Trade*, 1(4): https://doi.org/10.1186/s41072-016-0006-2

48. Ducruet C., Cuyala S., El Hosni A. (2018) Maritime networks as systems of cities: the long-term interdependencies between global shipping flows and urban development (1890-2010). *Journal of Transport Geography*, 66: 340-355.

49. Ducruet C., Ietri D., Rozenblat C. (2011) Cities in worldwide air and sea flows: A multiple networks analysis. *Cybergeo: European Journal of Geography*, 528.

50. Ducruet C., Itoh H. (2022a) Spatial network analysis of container port operations: the case of ship turnaround times. *Networks and Spatial Economics*, 22: 883-902.

51. Ducruet C., Itoh H. (2022b) The spatial determinants of innovation diffusion: Evidence from global shipping networks. *Journal of Transport Geography*, 101: 103358.

52. Ducruet C., Itoh H., Berli J. (2020) Urban gravity in the global container shipping network. *Journal of Transport Geography*, 85: 102729.

53. Ducruet C., Lee S.W., Ng A.K.Y. (2010a) Centrality and vulnerability in liner shipping networks: revisiting the Northeast Asian port hierarchy. *Maritime Policy and Management*, 37(1): 17-36.

54. Ducruet C., Notteboom T.E. (2012) The worldwide maritime network of container shipping: spatial structure and regional dynamics. *Global Networks*, 12(3): 395-423.

55. Ducruet C., Notteboom T.E. (2022) Revisiting port system delineation through an
analysis of maritime interdependencies among seaports. *GeoJournal*, 87: 1831-1859.

56. Ducruet C., Roussin S., Jo J.C. (2009) Political and economic factors in the evolution of North Korea's maritime connections. *Journal of International Logistics and Trade*, 7(1): 1-23.

57. Ducruet C., Rozenblat C., Zaidi F. (2010b) Ports in multi-level maritime networks: evidence from the Atlantic (1996-2006). *Journal of Transport Geography*, 18(4): 508-518.

58. Ducruet C., Wang L. (2018) China's global shipping connectivity: Internal and external dynamics in the contemporary era (1890-2016). *Chinese Geographical Sciences*, 28: 202-216.

59. Ducruet C., Zaidi F. (2012) Maritime constellations: a complex network approach to shipping and ports. *Maritime Policy and Management*, 39(2): 151-168.

60. Dui H., Zheng X., Wu S. (2021) Resilience analysis of maritime transportation systems based on importance measures. *Reliability Engineering & System Safety*, 209: 107461.

61. Earnest D.C., Yetiv S., Carmel S.M. (2012) Contagion in the transpacific shipping network: international networks and vulnerability interdependence. *International Interactions*, 38(5): 571-596.

62. Elayam M.M., Ray C., Claramunt C. (2022) A hierarchical graph-based model for mobility data representation and analysis. *Data & Knowledge Engineering*, 141: 102054.

63. Fan F., Zhang X., Yang W., Liu C. (2021) Spatiotemporal evolution of China's ports in the international container transport network under upgraded industrial structure. *Transportation Journal*, 60(1): 43-69.

64. Fang Z., Yu H., Lu F., Feng M., Huang M. (2018) Maritime network dynamics before and after international events. *Journal of Geographical Sciences*, 28: 937-956.

65. Feng X., Jiang H., Jiang L.P. (2020) Study on community detection of shipping network based on modularity. *Green, Smart and Connected Transportation Systems*, 365-374.

66. Ferrari C., Persico L., Tei A. (2022) Covid-19 and seaborne trade: The Italian perspective. *Research in Transportation Economics*, 93: 101162.

67. Filipiak. D., Wecel K., Strozyna M. Michalak M., Abramowicz W. (2020) Extracting maritime traffic networks from AIS data using evolutionary algorithm. *Business & Information Systems Engineering*, 62(5): 435-450.

68. Fontes F., Goncalves G. (2021) A variable neighbourhood decomposition search approach applied to a global liner shipping network using a hub-and-spoke with sub-hub structure. *International Journal of Production Research*, 59(1): 30-46.

69. Fraser D.R., Notteboom T.E., Ducruet C. (2016) Peripherality in the global con-
tainer shipping network: the case of the southern African container port system. *Geo-Journal*, 81(1): 139-151.

70. Freire Seoane M.J., Gonzalez-Laxe F., Montes C.P. (2013) Foreland determination for containership and general cargo ports in Europe (2007–2011). *Journal of Transport Geography*, 30: 56-67.

71. Frémont A. (2007) Global maritime networks: the case of Maersk. *Journal of Transport Geography*, 15(6): 431-442.

72. Fu Y., Li X., Li J., Yu M., Lu X., Huangpeng Q., Duan X. (2024) Multi-scale higher-order dependencies (MSHOD): Higher-order interactions mining and key nodes identification for global liner shipping network. *Journal of Marine Sciences and Engineering*, 12(8): 1305.

73. Fugazza M., Hoffmann J. (2017) Liner shipping connectivity as determinant of trade. *Journal of Shipping and Trade*, 2(1): https://doi.org/10.1186/s41072-017-0019-5.

74. Gadhia H.K., Kotzab H., Prockl G. (2011) Levels of internationalization in the container shipping industry: an assessment of the port networks of the large container shipping companies. *Journal of Transport Geography*, 19: 1431-1442.

75. Ge J., Wang X., Shi W. (2021) Link prediction of the world container shipping network: A network structure perspective. *Chaos*, 31(11): 113123.

76. Ge J., Fu Q., Zhang Q., Wan Z. (2022) Regional operating patterns of world container shipping network: A perspective from motif identification. *Physica A*, 607: 128171.

77. Gonzalez-Laxe F., Freire Seoane M.J., Montes C.P. (2012) Maritime degree, centrality and vulnerability: port hierarchies and emerging areas in containerized transport (2008–2010). *Journal of Transport Geography*, 24: 33–44.

78. Gou Y., Wang C. (2022) Identification and differentiation of the hierarchical structure of the Caribbean cruise shipping network based on route organization. *Sustainability*, 14(15): 9448.

79. Gou Y., Wang C., Bo A. (2024) Integration effect of shipping network based on shipping alliances. *Maritime Policy and Management*, 51(5): 745-760.

80. Guerrero D. Letrouit L., Montes C.P. (2022) The container transport system during Covid-19: An analysis through the prism of complex networks. *Transport Policy*, 115: 113-125.

81. Guerrero D., Niérat P., Thill J.C., Cohen E. (2021) Visualizing maritime connectivity at national level: the case of LSBCI links of West European countries. *Case Studies on Transport Policy*, 9(4): 1818-1824.

82. Guerrero D., Niérat P., Thill J.C., Cohen E. (2022a) Shifting proximities: visualizing
changes in the maritime connectivity of African countries (2006/2016). *Maritime Policy and Management*, https://doi.org/10.1080/03088839.2022.2116657

83. Guerrero D., Niérat P., Thill J.C., Cohen E. (2022b) Visualizing the maritime connectivity of African countries. *Journal of Transport Geography*, 101: 103355.

84. Guo J., Feng T., Wang S., Qin Y., Yu X. (2024a) Shipping network vulnerability assessment integrated with geographical locations. *Transportation Research Part D*, 130: 104166.

85. Guo J., Guo S., Lv J. (2022) Potential spatial effects of opening Arctic shipping routes on the shipping network of ports between China and Europe. *Marine Policy*, 136: 104885.

86. Guo J., Qin Y. (2022) Coupling characteristics of coastal ports and urban network systems based on flow space theory: Empirical evidence from China. *Habitat International*, 126: 102624.

87. Guo J., Wang S., Wang D., Liu T. (2017) Spatial structural pattern and vulnerability of China-Japan-Korea shipping network. *Chinese Geographical Sciences*, 27: 697-708.

88. Guo L., Yang Z. (2019) Relationship between shipping accessibility and maritime transport demand: the case of mainland China. *Networks and Spatial Economics*, 19: 149-175.

89. Guo Q., Wu Z., Ding C., Akbar M.W., Guo B. (2023) Unveiling the nexus between marine energy consumption, seaborne trade, and greenhouse gases emissions from international shipping. *Environmental Science and Pollution Research*, 30: 62553-62565.

90. Guo S., Lu J., Qin Y. (2024b) Analysis of the coupled spatial and temporal development characteristics of global liner shipping connectivity driven by trade. *Ocean and Coastal Management*, 251: 107071.

91. Guo S., Lyu J., Zhu X., Fan H. (2025) Multi-feature fusion for the evaluation of strategic nodes and regional importance in maritime networks. *Chaos, Solitons and Fractals*, 191: 115902.

92. He Y., Yang Y., Wang M., Zhang X. (2022) Resilience analysis of container port shipping network structure: The case of China. *Sustainability*, 14(15): 9489.

93. Hoffmann J., Saeed N., Sodal S. (2020) Liner shipping bilateral connectivity and its impact on South Africa's bilateral trade flows. *Maritime Economics & Logistics*, 22(3): 473-499.

94. Hu B., Zong G. (2013) Topology analysis of China's port shipping network. *Journal of Software*, 8: 2581-2586.

95. Hu Y., Zhu D. (2009) Empirical analysis of the worldwide maritime transportation
network. *Physica A*, 388(10): 2061-2071.

96. Hu Z.H. (2019) Vietnam's connectivity and embeddedness in the maritime silk road and global maritime network. *IEEE Access*, 7: 79592-79601.

97. Hu Z.H. (2020) Data-driven analytics for China's overseas construction projects in the contexts of the maritime silk road and global maritime network. *Complexity*, 8: 1-21.

98. Hu Z.H., Liu C.J., Lee P.T.W. (2020a) Analyzing interactions between Japanese ports and the Maritime Silk Road based on complex networks. *Complexity*, https://doi.org/10.1155/2020/371934

99. Hu Z.H., Liu C.J., Chen W., Wang Y.G., Wei C. (2020b) Maritime convection and fluctuation between Vietnam and China: A data-driven study. *Research in Transportation Business and Management*, 34: 100414.

100. Hu Z.H., Zheng Y.X., Hu J.J. (2022) Vietnam's maritime shipping ports: Using data-driven model analysis to determine connectivity and networking. *Journal of Coastal Research*, 38(3): 639-653.

101. Huang S., Sun T., Shi J., Gong P., Yang X., Zheng J., Zhuang H., Qi O. (2024a) Trading community analysis of countries' roll-on/roll-off shipping networks using fine-grained vessel trajectory data. *Sensors*, 24(22): 10.3390/s24227226

102. Huang Y., Hossein R.T., Gardner L. (2016) Modelling the global maritime container network. *Maritime Economics & Logistics*, 20: 400-420.

103. Huang Z., He Z., Zhao P., Zhang C., Niu Y., Guo W., Cui Y., Shao W. (2024b) The effects of tropical cyclone on the container shipping network: A case study of typhoon Ma-on (2022). *Transportation Research Part D*, 136: 104449.

104. Indriastiwi F., Hadiwardoyo S.P. (2021) Port connectivity model in the perspective of multimodal transport: A conceptual framework. *IOP Conference Series: Materials Science and Engineering*, 1052(1): 12008.

105. Ito H., Hanaoka S., Sugishita K. (2022) Structural changes in the cruise network by ship size in Northeast Asia. *The Asian Journal of Shipping and Logistics*, 38(4): 207-221.

106. Jarumaneeroj P., Lawton J.B., Svindland M. (2024) An evolution of the global container shipping network: port connectivity and trading community structure (2011-2017). *Maritime Economics & Logistics*, 26: 283-306.

107. Jarumaneeroj P., Ramudhin A., Barnett Lawton J. (2023) A connectivity-based approach to evaluating port importance in the global container shipping network. *Maritime Economics & Logistics*, 25: 602-622.

108. Jeon J.W., Duru O., Yeo G.T. (2019) Cruise port centrality and spatial patterns of cruise shipping in the Asian market. *Maritime Policy and Management*, 46(3): 257-276.

109. Jiang J., Lee L.H., Chew E.P., Gan C.C. (2015) Port connectivity study: an analysis
framework from a global container liner shipping network perspective. *Transportation Research Part E*, 73: 47-64.

110. Jiang L., Jia Y., Zhang C., Wang W., Feng X. (2019) Analysis of topology and routing strategy of container shipping network on “Maritime Silk Road”. *Sustainable Computing: Informatics and Systems*, 21: 72-79.

111. Jiang L., Wang G., Feng X., Yu T., Lei Z. (2024) Study on cascading failure vulnerability of the 21st-century Maritime Silk Road container shipping network. *Journal of Transport Geography*, 117: 103891.

112. Jin L., Chen Z., Wang X., Yao H., Liu C., Yu B. (2021) China's international liner shipping network evolution and vulnerability based on Automatic Identification System (AIS) data. *Transportation Research Board*, https://trid.trb.org/view/1759283

113. Jin L., Chen J., Chen Z., Sun X., Yu B. (2022a) Impact of COVID-19 on China's international liner shipping network based on AIS data. *Transport Policy*, 121: 90-99.

114. Jin L., Chen J., Sheu J.B., Zhou H., Jian Y., Yu B. (2022b) Impacts of national strategies on gateway ports: An empirical study in the Bohai Rim. *Transport Policy*, 117: 1-11.

115. Jung P.H., Thill J.C. (2022) Sea-land interdependence and delimitation of port hinterland-foreland structures in the international transportation system. *Journal of Transport Geography*, 99: 103297.

116. Jung P.H., Thill J.C. (2024) Global shrinkage of space and the hub-and-spoke system in the global trade network. *International Regional Science Review*, 47(3): https://doi.org/10.1177/016001

117. Kaluza P., Kölzsch A., Gastner M.T., Blasius B. (2010) The complex network of global cargo ship movements. *Journal of the Royal Society Interface*, 7(48): 1093-1103.

118. Kang D.J., Woo S.H. (2017) Liner shipping networks, port characteristics and the impact on port performance. *Maritime Economics & Logistics*, 19: 274-295.

119. Kang L., Wu W., Yu H., Su F. (2022) Global container port network linkages and topology in 2021. *Sensors*, 22(15): 5889.

120. Kanrak M., Lau Y., Ling X., Traiyarach S. (2023a) Cruise shipping network of ports in and around the emission control areas: a network structure perspective. *Maritime Business Review*, 8(4): 372-388.

121. Kanrak M., Lau Y., Zhou J., Ge J., Traiyarach S. (2023b) Empirical analysis of the cruise shipping network in Asia. *Sustainability*, 15(3): 2020.

122. Kanrak M., Nguyen H.O. (2022a) An analysis of connectivity, assortativity and cluster structure of the Asian-Australasian cruise shipping network. *Maritime Transport Research*, 3: 100048.
123. Kanrak M., Nguyen H.O. (2022b) Structure, characteristics and connectivity analysis of the Asian-Australasian cruise shipping network. *Maritime Policy and Management*, 49(6): 882-896.

124. Kanrak M., Nguyen H.O., Du Y. (2022) Analysis of the COVID-19 pandemic's impact on the cruise shipping network in the Asian-Australasian region. *Journal of International Logistics and Trade*, 20(1): 1-17.

125. Karountzos O., Kagkelis G., Kepaptsoglou K. (2023) A decision support GIS framework for establishing zero-emission maritime networks: the case of the Greek coastal shipping network. *Journal of Geovisualization and Spatial Analysis*, 7(16).

126. Kojaku S., Xu M., Xia H., Masuda N. (2019) Multiscale core-periphery structure in a global liner shipping network. *Scientific Reports*, 9(404): https://doi.org/10.1038/s41598-018-35922-2

127. Kölzsch A., Blasius B. (2011) Indications of marine bioinvasion from network theory: an analysis of the global cargo ship network. *The European Physical Journal B*, 84: 601-612.

128. Kosowska-Stamirowska Z., Ducruet C., Rai N. (2016) Evolving structure of the maritime trade network: evidence from the Lloyd's Shipping Index (1890-2000). *Journal of Shipping and Trade*, 1(10): https://doi.org/10.1186/s41072-016-0013-3

129. Kosowska-Stamirowska Z. (2020) Network effects govern the evolution of maritime trade. *Proceedings of the National Academy of Sciences* U.S.A., 117(23): 12719-12728.

130. Kuang Z., Liu C., Wu J., Wang Y.G. (2022) An effective distance-based centrality approach for exploring the centrality of maritime shipping network. Heliyon, 8(11): e11474.

131. Lam J.S.L., Yap W.Y. (2011) Dynamics of liner shipping network and port connectivity in supply chain systems: analysis on East Asia. *Journal of Transport Geography*, 19(6): 1272-1281.

132. LaRock T., Xu M., Eliassi-Rad T. (2022) A path-based approach to analyzing the global liner shipping network. EPJ Data Science, 11(18): https://doi.org/10.1140/epjds/s13688-022-00331-z

133. Lau Y.Y., Kanrak M., Ng A.K.Y., Ling X. (2023) Arctic region: analysis of cruise products, network structure, and popular routes. *Polar Geography*, 46(2-3): 157-169.

134. Lau Y.Y., Kanrak M., Poo M.C.P., Yang Z., Yang Z. (2024) Analysis of the ferry service network in Hong Kong. *Island Studies Journal*, 19(2): https://doi.org/10.24043/001c.125990

135. Lee H.C., Park S.H., Yeo G.T. (2021) A study on the evaluation of coastal shipping's centrality using a social network analysis. *Journal of Digital Convergence*, 19(2): 69-81.
136. Lee J.S., Kim T.H., Park Y.G. (2023) Maritime transport network in Korea: Spatial-temporal density and path planning. Journal of Marine Science and Engineering, 11(12): 2364.

137. Lendjel E., Mareï N. (2021) The complementarity between dhow shipping networks and standard shipping lines in the gulfs of Persia, Oman and Aden. Cybergeo: European Journal of Geography, 984: https://journals.openedition.org/cybergeo/37025

138. Lenzen M., Tzeng M., Floerl O., Zaiko A. (2023) Application of multi-region input-output analysis to examine biosecurity risks associated with the global shipping network. Science of the Total Environment, 854: 158758.

139. Li C., Yang X., Yang D. (2025) Port vulnerability to natural disasters: An integrated view from hinterland to seaside. Transportation Research Part D: Transport and Environment, 139: 104563.

140. Li D., Liu S. (2022) The value of connectivity: Evolution of global shipping services networks and the dynamics of global value chains. Frontiers in Business, Economics and Management, 4(3): 90-93.

141. Li J., Zhao H., Xu B. (2022) Optimization of container shipping network under RCEP. Journal of Marine Science and Engineering, 10(7): 873.

142. Li L., Wei C., Liu J., Chen J., Yuan H. (2024a) Assessing port cluster resilience: Integrating hypergraph-based modeling and agent-based simulation. Transportation Research Part D, 136: 104459.

143. Li Q., Zhang Z., Chen Z., Chen L., Zhang J., Chen K., Wang Y., Gao Y. (2020a) Evolutionary dynamics of competition among ports in networks. *Modern Physics Letters B*, 34(24): 2050248.

144. Li W., Bai X., Yang D., Hou Y. (2023a) Maritime connectivity, transport infrastructure expansion and economic growth: A global perspective. *Transportation Research Part A*, 170: 103609.

145. Li W., You Z., Lou K., Wang Z. (2023b) Development of GPCN model to assess domestic and international competition patterns of coastal container ports in China. *Systems*, 11(1): 19.

146. Li X. (2024) Where are the cruise ships? mobility and immobility of cruises under COVID-19. *Plos One*, 19(11): e0303216.

147. Li X., Li C., Long Z. (2024b) Western North American cruise shipping network: Space structure and system. *Complexity*, 2024(1): 9190221.

148. Li X., Wang C., Ducruet C. (2020b) Globalization and regionalization: empirical evidence from itinerary structure and port organization of world cruise of Cunard.
*Sustainability*, 12(19): 7893.

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