Qube a quick algorithm for updating betweenness centrality cowgirl up dating
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Our algorithm experimentally speeds up the calculation of betweenness centrality (after updation) from 7 to 412 times, for real graphs, in comparison to the currently best known technique to find betweenness centrality.
Betweenness centrality of a vertex (edge) in a graph is a measure for the relative participation of the vertex (edge) in the shortest paths in the graph.
In particular, we propose a new upper bound on the vertex diameter for weighted undirected graphs.
For both weighted and unweighted graphs, we also propose the first fully-dynamic algorithms that keep track of this upper bound.
Experimental results on real graphs show that the proposed algorithm efficiently update betweenness centrality and detect communities in a graph.
The betweenness centrality of a vertex in a graph is a measure for the participation of the vertex in the shortest paths in the graph.
Using approximation, our algorithms are the first to make in-memory computation of betweenness in fully-dynamic networks with millions of edges feasible.Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths.Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed.The proposed update algorithm substantially reduces the number of shortest paths which should be re-computed when a graph is changed.In addition, we adapt a community detection algorithm using the proposed algorithm to show how much benefit can be obtained from the proposed algorithm in a practical application.