## 5 Discussion  
The global tourism development has made the world tourism industry a network. The network structure significantly influences the dynamical characteristics of tourism under the perspective of networks [78], but the impact characteristics are not clear yet. In order to improve the achievement of international tourism and ensure that it is well-performing, this study applies the complex network theory to detect how the GNT changes and how the GNT structure affects the international tourism industrial performance. Firstly, the GNT based on international tourism arrivals of 222 regions from 1995 to 2019 has been constructed. In terms of the GNT evolution, the results show that the size of the GNT has been gradually expanding both from the numbers of nodes and edges as well as the total number of tourists. The APL increased, while the CLC and SW decreased at the global level, which are partly similar with the findings of [PERSON] [5] and [PERSON] [67], which also indicate that the network structure may affect the number of tourists. Then, the work of the GNT structure's influence analyses is divided into two parts. The study employs the AITA (the amount of average international tourism arrivals) as the overall performance indicator and global network structure measurements (density, average path length, clustering coefficient, and small-worldness) as the variables to show how they affect each other through Pearson's correlations test. The results indicate that each pair among them has a significant correlation, and the effect of density and clustering coefficient on the AITA is significantly positive, while the average path length and small-worldness have a significantly negative impact. That means if the global tourism network has either a higher density of edges between the nodes (regions) or a high degree of clustering, it will generate a larger amount of tourists overhaul. However, if the network has a high average shortest distance between all pairs of nodes or a high degree of small-world nature, the overall number of tourists will be lower. The similar outcomes are shown in other fields of research, e.g., team-game network [37], knowledge diffusion network [79] and policy  
Figure 8: The coefficients of all individual measurements on performance indicator from 1995 to 2019.  
network [35], which demonstrates that these real-world networks have general characters in the structures. What is more, from the fitting model test, the exponential models can well fit with the relationship of DE and CLC between AITA, respectively. Simultaneously, the relationship between AITA with APL and SW shows a power model, which means they have different influencing degrees, and this can be helpful for predicting the global tourism performance from a network structure. Secondly, we explore how the individual's network structure characteristic affects an individual's tourism performance through the gravity model and mixed-effect model. The results reflect the individual's weighted degree centrality, the closeness centrality, and the betweenness centrality, which positively influence the individual's international tourism arrivals with significance at the level of 0.001. Meanwhile, the eigenvector centrality and local clustering coefficient work negatively on individuals' performance. That is to say, if a region is connected with more other regions, that means it is close to others and helps link to other pairs of regions, which can bring more international arrivals. However, if the region only relies on some more influential regions' connections or has some competitive neighbors, its attraction for international tourists will decrease. Additionally, all the impact degrees from the same variable of target countries are higher than those of the sources. That means that a region needs to change its own roles in the global tourism network to gain more arrivals instead of only relying on cooperative partners.