Identifying Central Nodes in Directed and Weighted Networks

An issue of critical interest in complex network analysis is the identification of key players or important nodes. Centrality measures quantify the notion of importance and hence provide a mechanism to rank nodes within a network. Several centrality measures have been proposed for un-weighted, un-directed networks but applying or modifying them for networks in which edges are weighted and directed is challenging. Existing centrality measures for weighted, directed networks are by and large domain-specific. Depending upon the application, these measures prefer either the incoming or the outgoing links of a node to measure its importance. In this paper, we introduce a new centrality measure, Affinity Centrality, that leverages both weighted in-degrees as well as out-degrees of a node's local neighborhood. A tuning parameter permits the user to give preference to a node's neighbors in either incoming or outgoing direction. To evaluate the effectiveness of the proposed measure, we use three types of real-world networks - migration, trade, and animal social networks. Experimental results on these weighted, directed networks demonstrate that our centrality measure can rank nodes in consonance to the ground truth much better than the other established measures.

Automated summary

The issue of identifying key players or important nodes in complex network analysis is addressed through the introduction of a new centrality measure called Affinity Centrality, which leverages both weighted in-degrees and out-degrees of nodes to evaluate their importance. Experimental results on real-world networks demonstrate that this centrality measure can rank nodes more accurately compared to other established measures.