Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence 2017
DOI: 10.24963/ijcai.2017/59
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Attachment Centrality for Weighted Graphs

Abstract: 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 typicall… Show more

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Cited by 7 publications
(5 citation statements)
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“…To the best of our knowledge, the techniques proposed by us are new and they can be used to further axiomatic analysis of centrality measures. In fact, the early versions of these results were already used by us in the work on axiomatic foundations of the Attachment centrality's extension to weighted graphs (Sosnowska & Skibski, 2017).…”
Section: Discussionmentioning
confidence: 99%
“…To the best of our knowledge, the techniques proposed by us are new and they can be used to further axiomatic analysis of centrality measures. In fact, the early versions of these results were already used by us in the work on axiomatic foundations of the Attachment centrality's extension to weighted graphs (Sosnowska & Skibski, 2017).…”
Section: Discussionmentioning
confidence: 99%
“…The authors, through experimentation, conclude that their model is competitive to other similar ones [101]. Even axiomatic approaches have been proposed (see [103] and [104]).…”
Section: Introductionmentioning
confidence: 94%
“…The authors, through experimentation, conclude that their model is competitive to other similar ones [34]. Even axiomatic approaches have been proposed (see [35,36]).…”
Section: Introductionmentioning
confidence: 95%