2014 AlmostLinearTimeAlgorithmsforAd

Subject Headings:

Notes

Betweenness centrality measures the importance of a vertex by quantifying the number of times it acts as a midpoint of the shortest paths between other vertices. This measure is widely used in network analysis. In many applications, we wish to choose the k vertices with the maximum adaptive betweenness centrality, which is the betweenness centrality without considering the shortest paths that have been taken into account by already-chosen vertices. All previous methods are designed to compute the betweenness centrality in a fixed graph. Thus, to solve the above task, we have to run these methods $k$ times. In this paper, we present a method that directly solves the task, with an almost linear runtime no matter how large the value of k. Our method first constructs a hypergraph that encodes the betweenness centrality, and then computes the adaptive betweenness centrality by examining this graph. Our technique can be utilized to handle other centrality measures. We theoretically prove that our method is very accurate, and experimentally confirm that it is three orders of magnitude faster than previous methods. Relying on the scalability of our method, we experimentally demonstrate that strategies based on adaptive betweenness centrality are effective in important applications studied in the network science and database communities.

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 AlmostLinearTimeAlgorithmsforAd	Yuichi Yoshida			Almost Linear-time Algorithms for Adaptive Betweenness Centrality Using Hypergraph Sketches				10.1145/2623330.2623626		2014