# Topology of the World Tourism Web
## 3 Results and Discussion
### Network Topology and Structure
#### 3.1.1 Basic Network Measures
However, international trade (or trade in general, for that matter) has the longest tradition, and those links have been forming over a very long time. On the other hand, international flows in education are a more recent development (also due to availability of education and efforts to increase the share of highly educated persons in the population). The migration flows might change due to political (in)stabilities, the economic situation, and globalization trends. It seems that the numerosity of formed connections is an indication of the development stage, which in the case of tourism means that the possibilities for growth are high. However, the insight into migration densities shows that people's movements are sensitive to disturbances and global trends, which leads to the expectation of a lower density of the tourism network in 2020 (after the pandemic occurrence) and its increase in subsequent years along with presumably supporting global tourism trends. The average of the nodes' clustering coefficients indicates that for most of the nodes, their neighbors also have a direct link between them. However, this also means that the importance of most links in the network is relatively small, as their neighbors are \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Not Weighted** & **Weighted** \\ \hline Average degree & 70.339 & 70.339 \\ Average weighted degree & / & 5,984,652.455 \\ Average path length & 1.6579 & 1.6579 \\ Diameter & 4 & 4 \\ Radius & 2 & 2 \\ Density & 0.292 & 0.292 \\ Average eccentricity & 2.227 & 2.227 \\ Average clustering coefficient & 0.8279 & 0.8279 \\ Number of triangles & 423,624 & 423,624 \\ Number of paths (length 2) & 1,838,537 & 1,838,537 \\ Clustering coefficient (triangle method) & 0.6912 & 0.6912 \\ Number of strongly connected components & 48; 1 (195); 47 (1) & 48; 1 (195); 47 (1) \\ Number of weakly connected components & 1 & 1 \\ Diameter of weakly connected components & 3 & 3 \\ Radius of weakly connected components & 2 & 2 \\ Eccentricity of largest weakly connected components & 2 (187), 3 (55) & 2 (197), 3 (55) \\ Number of nodes in periphery & 55 & 55 \\ Number of nodes in center & 187 & 187 \\ Number of clusters (Leiden CPM; random seed) & 6 (qualify:0.978) & 2 (qualify:0.999) \\ Modularity (random seed) & 0.09 & 0.52 \\ Number of communities & 4 & 6 \\ Number of nodes in communities & 75, 71, 83, 13 & 38, 54, 16, 27, 67, 40 \\ \hline \hline \end{tabular} Source: authors’ calculation using Cephi and R. Source: authors.  
\end{table} 
Table 1: Tourism network parameters. already connected to each other. Cluster analysis reveals the existence of six clusters for the not-weighted graph, but observing in combination with high clustering coefficients (clustering coefficients vary from 0.4639 to 1), it is likely that overlaps and embeddedness occur. The weighted graph reveals only two clusters, indicating higher cohesion when applying weights. The clustering coefficient calculated over transitive triples (cliques of size 3) confirms the same insight, indicating that on average, a fraction of those triplets occurs in roughly two-thirds of the cases (while individual clustering coefficients vary from 0.3066 to 1). Our findings are in line with previous research of world tourism networks [7], which reveals several clusters by different criteria. The clustering is present in other global networks, as well. For example, in the trade network, individual cluster coefficients are close to 1, and for the international financial network, they vary from 0.7 to 0.85 [35]. In the migration network, the average clustering coefficient increased over time and reached 0.76 in 2000 [37]. While the average clustering coefficient is high in this network, it can be observed that individual clustering coefficients vary more in comparison to other global networks, suggesting the necessity for a deeper insight into the nodes' position in the network. Additional insight may be obtained if we switch to the micro approach, observing node statistics (Supplementary Files S1 and S2).