# Topology of the World Tourism Web
## 3 Results and Discussion
### Network Topology and Structure  
#### 3.1.1 Basic Network Measures  
A tourism network is a connected network where at least one link exists to each node. The average degree points out that on average, nodes are connected to more than half of the nodes in the network. The average in-degree accordingly suggests that countries, on average, receive tourists from more than a quarter of the countries in the network, or in the case of the average out-degree, tourists go to over a quarter of the countries in the network (Table 1). The average in-degree and average out-degree are equal as the countries are both source and target and the total in the numerator is the same. The in-degree range is 233 (from 0 to 233), with an average of 70.339, and the standard deviation is 75 degrees, which denotes high variability. The out-degree range is 187 (from 1 to 188), and the standard deviation is 39.77 degrees. Data variation is even higher for weighed degrees, with an in-degree standard deviation of 15,365,473.17 degrees and an out-degree standard deviation of 16,982,264.39 degrees. The network diameter is small in comparison to the network size, meaning that the graph can be traversed by four connections. Accordingly, the nodes' eccentricities are quite small and the average eccentricity is 2.227, meaning that for most countries, the longest shortest path has a length of 2. The average path length is also very small (1.66), suggesting that on average, most nodes can be reached by the shortest paths of lengths 1 or 2 from any other node, which points out to relative efficiency of the graph. This is also an indication of subgraph star structures along with cliques of 3 within the network. Given the small diameter, it is expected that the network is not very complex, yet other measures will address that question. Previous research [7] revealed diameters of 6-14 for the groups of top \(k\) countries in inbound and outbound tourism. While such methodological approach disregards some of the nodes and links, the significant difference indicates the increase in link formation. Moreover, the authors report an average geodesic of 1.669-3.799 for observed groups of the countries, where the lowest value belongs to the top one inbound group of countries and corresponds to our calculation. This could  
Figure 1: Interactive world tourism web. Note: The map displays only the arcs with weight \(>\) 1000. Source: authors. Available online: [[https://www.mdpi.com/2076-3417/11/5/2253/s1](https://www.mdpi.com/2076-3417/11/5/2253/s1)]([https://www.mdpi.com/2076-3417/11/5/2253/s1](https://www.mdpi.com/2076-3417/11/5/2253/s1)) (accessed on 7 March 2021). mean that the interconnectivity of the countries increased due to tourism development and shortened the average shortest path for the tourism network in 2018 to the level that was present only for the top one inbound group of countries in 2014. The graph density indicates a relatively sparse graph. The number of existing links is less than a third of all possible ties, given the number of the nodes. When comparing the findings to [PERSON] and [PERSON]'s [7] findings, who found that the top \(k\) inbound and outbound countries analyzed separately show a density of 0.003 to 0.014, it seems that the connectivity in tourism has intensified over the years. However, these differences may also result from the different methodological approach. In comparison to other global networks, this might be an indication of sector development. For example, in the education network, the density was 0.1235 in 2011 [38]; in the migration network, it was 0.47 for the year 2000 [37] and 0.046 in 2019 according to [PERSON] [62]; in the financial network, density varies between 0.2 and 0.6 [34]; and for the international trade network, the density reaches an amazing 0.98 [35]. International trade has the most realized links, meaning that almost each country trades with every other country.