Attachment Centrality for Weighted Graphs
Attachment Centrality for Weighted Graphs
Jadwiga Sosnowska, Oskar Skibski
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 416-422.
https://doi.org/10.24963/ijcai.2017/59
Measuring how central nodes are in terms of connecting a network has recently received increasing attention in the literature. While a few dedicated centrality measures have been proposed, Skibski et al. [2016] showed that the Attachment Centrality is the only one that satisfies certain natural axioms desirable for connectivity. Unfortunately, the Attachment Centrality is defined only for unweighted graphs which makes this measure ill-fitted for various applications. For instance, covert networks are typically weighted, where the weights carry additional intelligence available about criminals or terrorists and the links between them. To analyse such settings, in this paper we extend the Attachment Centrality to node-weighted and edge-weighted graphs. By an axiomatic analysis, we show that the Attachment Centrality is closely related to the Degree Centrality in weighted graphs.
Keywords:
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