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Table 2 Social network analysis metrics that allow the identification of key players in wildlife trafficking networks

From: Federated databases and actionable intelligence: using social network analysis to disrupt transnational wildlife trafficking criminal networks

Metric Description
Degree centrality The number of links directly connected to that player
Betweenness centrality v i is defined as follows. Consider the j th pair of players (v , v ′′) j for which v′ ̸= v i and v′′ ̸= v i . Let T j be the number of shortest paths between v and v ′′ . Let n j be the number of these paths that contain player i. The betweenness centrality of v i is \( {\displaystyle \sum_{j=1}^m\frac{n_j}{T_j}} \) where m is the number of player-pairs that connect by at least one path [35]. The measure incorporates by re-defining the length of a path between players v i and v j to be the sum of the link weight inverses across all of the links in the path [44]. A player with high betweenness centrality has control of information propagation in a network
Eigenvector centrality Let W be the weighted-link adjacency matrix. Let e be the first eigenvector of W. The eigenvector centrality of player v i is the i th component of e [35]. This metric measures a player’s influence by measuring how easily information can flow between a player and all other players regardless of the path taken [43]
Network connectedness The largest eigenvalue of W. Let CI be this index
Gould-Fernandez total brokerage score The GF total brokerage score for vi is the number of player pairs, v, v” for which (v, vi) and (vi, v”) are both in E but the link (v, v’) is not in E [35]. A player with a high brokerage score functions as an intermediary (or broker) for many pairs of players not directly connected to each other