Fast approximation of centrality
David Eppstein,Joseph Wang +1 more
- pp 228-229
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TLDR
In this paper, a randomized approximation algorithm for centrality in weighted graphs was proposed, which estimates the centrality of all vertices with high probability within a (1 + ∈) factor in nearlinear time.Abstract:
Social studies researchers use graphs to model group activities in social networks. An important property in this context is the centrality of a vertex: the inverse of the average distance to each other vertex. We describe a randomized approximation algorithm for centrality in weighted graphs. For graphs exhibiting the small world phenomenon, our method estimates the centrality of all vertices with high probability within a (1 + ∈) factor in near-linear time.read more
Citations
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Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems
TL;DR: A simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags is discovered.
Book ChapterDOI
Centrality measures based on current flow
Ulrik Brandes,Daniel Fleischer +1 more
TL;DR: It is proved that the current-flow variant of closeness centrality is identical with another known measure, information centrality, and improved algorithms for computing both measures exactly are given.
Journal ArticleDOI
Path problems in temporal graphs
TL;DR: The shortcomings of classic shortest path in a temporal graph are shown, various concepts of "shortest" path for temporal graphs are studied, and efficient algorithms to compute these temporal paths are proposed.
Proceedings ArticleDOI
Parallel Algorithms for Evaluating Centrality Indices in Real-world Networks
David A. Bader,Kamesh Madduri +1 more
TL;DR: These are the first parallel implementations of these widely-used social network analysis metrics and it is demonstrated that it is possible to rigorously analyze networks three orders of magnitude larger than instances that can be handled by existing network analysis (SNA) software packages.
Ecient Computation of the Shapley Value for Game-Theoretic Network Centrality
Abstract: The Shapley value--probably the most important normative payoff division scheme in coalitional games--has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.
References
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TL;DR: In this paper, Factoring and weighting approaches to status scores and clique identification were proposed, and the results showed that the weighting approach is more accurate than the factoring approach.