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Centrality is a concept often used in social network analysis to study different properties of networks that are modeled as graphs. We present a new centrality metric called Localized Bridging Centrality (LBC). LBC is based on the Bridging Centrality (BC) metric that Hwang et al. recently introduced. Bridging nodes are nodes that are located in between highly connected regions. LBC is capable of identifying bridging nodes with an accuracy comparable to that of the BC metric for most networks. As the name suggests, we use only local information from surrounding nodes to compute the LBC metric, while, global knowledge is required to calculate the BC metric. The main difference between LBC and BC is that LBC uses the egocentric definition of betweenness centrality to identify bridging nodes, while BC uses the sociocentric definition of betweenness centrality. Thus, our LBC metric is suitable for distributed computation and has the benefit of being an order of magnitude faster to calculate in computational complexity. We compare the results produced by BC and LBC in three examples. We applied our LBC metric for network analysis of a real wireless mesh network. Our results indicate that the LBC metric is as powerful as the BC metric at identifying bridging nodes that have a higher flow of information through them (assuming a uniform distribution of network flows) and are important for the robustness of the network.
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Soumendra Nanda and David Kotz, "Localized Bridging Centrality for Distributed Network Analysis ." Dartmouth Computer Science Technical Report TR2008-612, January 2008.
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