Deconstructing centrality: thinking locally and ranking globally in networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Between ness centrality measures how critical a node is to information ï卢聜ow in a network. A node is critical (and hence should have high betweeness) if it is on many shortest paths. Two shortcomings of such a measure are:(i) It ignores nodes on â聙聹almost shortestâ聙聺 paths, (ii) It assumes that a node can provide the same attention to information ï卢聜ow through each of those shortest paths, no matter how many shortest paths the node controls. There have been attempts to address these concerns in the literature, with partial success. We provide a new measure, attentive between ness centrality (ABC), that measures criticality by the amount of attention a node devotes to the information ï卢聜ow between other nodes. Our measure addresses both the aforementioned concerns and can be computed efï卢聛ciently. It performs as well or better than between ness centrality on both stylized networks and large scale real data networks, and hence provides a useful tool for measuring node criticality.