Abstract
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
Highlights
Data analysts from diverse domains represent relationships or ties between entities using graph-based network models
We evaluate the role of central nodes delivered by the proposed centrality measure on the community structure of real-world networks (Section IV-D)
What is the role of topological central nodes on the community structure? We examined this question by extracting communities of the six networks and studied their evaluation in terms of important nodes delivered by the proposed centrality measure
Summary
Data analysts from diverse domains represent relationships or ties between entities using graph-based network models. In world trade networks, where links between nations represent the exchange of commodities, tie strength is the cash flow and its direction indicates either import or export [6]. When both the strength and direction of ties are available, modeling data as weighted, directed network can be more elucidative and revelatory. Several centrality measures have been formulated to quantify the notion of central nodes in un-weighted/ weighted, un-directed networks and are surveyed in [7], [3], [4], [5]. Very few measures exist for such networks, and the area remains under-explored
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