Efficient algorithms for updating betweenness centrality in fully dynamic graphs

• Devise an algorithm for updating betweenness centrality in fully dynamic graphs.
• Recalculate centrality without computing all pairs shortest paths in the entire graph.
• Adapt a highest centrality edge finding algorithm based on the proposed algorithm.
• Adapt a community detection algorithm using the proposed algorithms.
• Experimental results show the proposed algorithm outperforms existing algorithms.

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. Betweenness centrality is widely used in various areas such as biology, transportation, and social networks. In this paper, we study the update problem of betweenness centrality in fully dynamic graphs. 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. Experimental results on real graphs show that the proposed algorithm efficiently update betweenness centrality and detect communities in a graph.