Res derived from global flow networks are a proxy of socioeconomic activities and therefore highly correlated with the explored indicators. It is an open question as to whether a highly central position in the network leads to favourable socio-economic outcomes or vica-versa. The structural connectedness of a country in the global network represents the number of opportunities a country has to exchange goods, information and resources with our countries–the more opportunities, the higher the exchange and therefore socioeconomic benefit. An analogous relation between the social network of an individual and that individual’s poverty score supports this hypothesis [14]. A broad longitudinal study would be necessary to assert whether a country’s growth in connections precedes its economic growth or vice versa which is beyond the scope of this work. Next, we will look at the community structure of countries ASP015K dose across networks and evaluate their community multiplexity to show that countries with U0126 web similar socioeconomic profiles tend to cluster together, much like in social networks.Global Community AnalysisIn the previous section we related network measures to various socioeconomic indicators, showing that metrics such as the network degree can be used to estimate wellbeing at a national level. In this section, we further examine the connectedness between pairs of countries through community structure across network layers as a form of socioeconomic similarity. We use the Louvain modularity optimisation method [40] for community detection in each individual network,PLOS ONE | DOI:10.1371/journal.pone.0155976 June 1,14 /The International Postal Network and Other Global Flows as Proxies for National WellbeingFig 8. Country community membership for each network. doi:10.1371/journal.pone.0155976.gwhich takes into account the tie strength of relationships between countries and finds the optimal split in terms of disconnectedness in the international network. This returns between 4? communities for each network, the geographical distribution of which is shown in Fig 8. Although communities naturally seem to be very driven by geography in physical flow networks, this is not the case in digital networks where communities are geographically dispersed. This is an indication of the difference in the way countries connect through post, trade, migration and flights rather than on the IP and social media networks. However, what does it mean for two countries to be both members of the same network community? Common community membership indicates a level of connectedness between two countries, which is beyond the randomly expected for the network. It is often observed that nodes in the same communities share many similar properties, therefore it can be expected that pairs of nodes which share multiple communities across networks are even more similar. In this work, we measure the overlap in pairwise membership between pairs of countries across our six networks as the community multiplexity index, a measure of socioeconomic similarity. Our hypothesis is that countries that are paired together in communities across more networks are more likely to be socioeconomically similar. We measure similarity here as the absolute difference between each indicator from the previous section for two countries and plot that against their community multiplexity. For example, the United States has an average life expectancy of 70 years, whereas Afghanistan has an average life expe.Res derived from global flow networks are a proxy of socioeconomic activities and therefore highly correlated with the explored indicators. It is an open question as to whether a highly central position in the network leads to favourable socio-economic outcomes or vica-versa. The structural connectedness of a country in the global network represents the number of opportunities a country has to exchange goods, information and resources with our countries–the more opportunities, the higher the exchange and therefore socioeconomic benefit. An analogous relation between the social network of an individual and that individual’s poverty score supports this hypothesis [14]. A broad longitudinal study would be necessary to assert whether a country’s growth in connections precedes its economic growth or vice versa which is beyond the scope of this work. Next, we will look at the community structure of countries across networks and evaluate their community multiplexity to show that countries with similar socioeconomic profiles tend to cluster together, much like in social networks.Global Community AnalysisIn the previous section we related network measures to various socioeconomic indicators, showing that metrics such as the network degree can be used to estimate wellbeing at a national level. In this section, we further examine the connectedness between pairs of countries through community structure across network layers as a form of socioeconomic similarity. We use the Louvain modularity optimisation method [40] for community detection in each individual network,PLOS ONE | DOI:10.1371/journal.pone.0155976 June 1,14 /The International Postal Network and Other Global Flows as Proxies for National WellbeingFig 8. Country community membership for each network. doi:10.1371/journal.pone.0155976.gwhich takes into account the tie strength of relationships between countries and finds the optimal split in terms of disconnectedness in the international network. This returns between 4? communities for each network, the geographical distribution of which is shown in Fig 8. Although communities naturally seem to be very driven by geography in physical flow networks, this is not the case in digital networks where communities are geographically dispersed. This is an indication of the difference in the way countries connect through post, trade, migration and flights rather than on the IP and social media networks. However, what does it mean for two countries to be both members of the same network community? Common community membership indicates a level of connectedness between two countries, which is beyond the randomly expected for the network. It is often observed that nodes in the same communities share many similar properties, therefore it can be expected that pairs of nodes which share multiple communities across networks are even more similar. In this work, we measure the overlap in pairwise membership between pairs of countries across our six networks as the community multiplexity index, a measure of socioeconomic similarity. Our hypothesis is that countries that are paired together in communities across more networks are more likely to be socioeconomically similar. We measure similarity here as the absolute difference between each indicator from the previous section for two countries and plot that against their community multiplexity. For example, the United States has an average life expectancy of 70 years, whereas Afghanistan has an average life expe.
