approach, we begin reducing the level of complexity of the network’s structure by partitioning it into communities (sets of nodes densely connected to each other and more sparsely connected to the rest of the network).<sup>24</sup> In other words, a community is a group (cluster) of countries with tightly interrelated migration linkages among them that are less interconnected with countries outside the group (cluster).

Figure 14 displays the communities identified based on the network of origin and destination countries of international migrants in Libyan provinces.<sup>25</sup> Colors are used to identify provinces belonging to the same community of the migration network (areas characterized by the same composition of migrants and the same preferences as for destination countries).

The results indicate that provinces belonging to the same community (characterized by the presence of migrants originating in similar countries and having similar preferences for the destination country) are located close to each other. This suggests the presence of specific migrant passages running across the country. For instance, figure 14a indicates the presence of at least three definite routes: the Western route (aquamarine), beginning in the province of Ghat and ending in the provinces of Zwara and Aljfara; the Eastern route (red), from Alkufra to Tobruk; and the Central route (green), from Murzuq to Tripoli. A major change occurred in 2018 (figure 14b). The Central (violet) and Eastern (green) routes remained almost unchanged, but the Western route split into two different corridors. The first connected the province of Wadi Ashshati to Misrata (light green), and the second connected the eastern province of Nalut to the central province of Sirt and the western provinces of Benghazi and Al Jabal Al Akhdar (red). This might indicate that, from 2017 to 2018, there was a shift in migrants' routes, and eastern cities became passage points to reach the other side of the country.

# **6. Conclusion**

This paper has presented the first analysis of migration in Libya. To analyze this complex phenomenon, we have used DTM data of IOM and a rich toolkit that combines spatial statistical analysis and network analysis. Methods from spatial statistics were used to analyze location choices and to identify common patterns in migration movements and the presence of hotspots and clusters. Social network analysis was used to map the network of migration movements, determine the level of migratory pressure in different provinces, and identify the formation of network hubs.

> 24 Community search to study networks of migration flows has been used in Davis et al. 2013; Peres et al. 2016.

> 25 Several community detection algorithms have been proposed (see Fortunato 2010). The choice of one algorithm over another is usually data driven and determined by the algorithm’s ability to produce a meaningful community structure, with significant within-community cohesion and between-community separation (Clauset, Newman, and Moore 2004). A standard measure used to determine algorithm fit to the data is modularity, a metric ranging between -1 and 1, with lower values reflecting poor community structure (many between-community edges and few within-community edges) and values closer to 1 indicating good community structure. In the case of the Libyan migration network, the algorithm producing the best partition is the spinglass algorithm (Reichardt and Bornholdt 2006), which returns a modularity value greater than 0.2 for 2017 and 2018.

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