## 6 Conclusions  
The findings of this research have supported the argument that the tourism network structure can affect the tourism performance significantly. However, the different influential characteristics are shown from different structural metrics from the global and individual level. The results indicate the network structure can change the global international tourism arrivals, which means the global tourism development can be described as the effect of the global tourism network structure on the tourism industrial performance. The methodology gives a useful application of network theory in the tourism field by a long-time period statistic and a new quantitative way to explain how the global network influences global and individual tourism development. The work demonstrates the various effects of the network structure from nine hypotheses divided into the global and individual levels. The different characteristics of tourism network structure can reflect different levels of tourism industrial development status, which helps researchers further understand each measurement's means and impacts as well as contributes to the network theory. What is more, the differences between the measurements of the sources and targets explain that a pair node (region) in the tourism network have different effects on each other, where the target's structural characteristics have the larger impact, which has not been considered before. According to these findings, some practical suggestions and implications can be proposed for global tourism development. Such efforts are believed to bring tourism economic developments and increase countries' awareness of screened cooperators in the global context. For a country that wants to increase their international performance, the most important thing is to improve its own network structural characteristics, such as raising the degree centrality (contact with more countries), closeness centrality (direct contact with other countries not based on others' links), and betweenness centrality (help more region pairs construct the shortest path to each other), as well as declining its eigenvector centrality (decrease the attachment with regions that have a higher connection and influence to others) and decreasing the local clustering coefficient (establish association with some end-pointregions). By comparison among coefficients, in global tourism networks, the closeness centrality, especially from the source, is the most important in international tourism performance. Thus, keeping a close tie with high closeness countries is an effective strategy for attracting international tourists. Secondly, from the targets, some countries (such as the United States, China mainland, and Canada) with higher degree/closeness/betweenness centralities and lower eigenvector centralities and clustering coefficients will be popular for those who want to increase their own tourism performance. Certainly, strengthening ties between regions to raise the global tourism network's density and clustering coefficient can increase global tourism and bring benefits to all the regions, which need international cooperation and close coordination. Meanwhile, this research also has some limitations that need to be further researched. First, we only set the tourism network within global tourism, as we know that the tourism network can be divided into many types, and the network effects should be discussed in other issues to help to solve complex tasks. Second, we use longitudinal quantitative methods to analyze the effect of network structure; why the coefficients are changeable and how they are affected by other conditions are covered. Third, we separate the overall performance and individual performance in this study, but they work simultaneously and must influence each other in reality. We divided the two levels into different models and discussed them, respectively, which can cause bias in the effect estimation.