Edge Betweenness Centrality on Trees

Computing the edge betweenness centrality is an important step in a great deal of the analysis tasks of community structures in complex networks. It mostly serves as a measure for the traffic or flow of a particular edge in connecting various parts or communities together. Various algorithms that compute the edge betweenness centrality in general graphs exist but they are expensive. In this paper, we design an algorithm that takes advantage of the structure of tree graphs to compute the edge betweenness centrality more efficiently in such graphs and perform experiments on random graphs.

[1]  Katerina Potika,et al.  Weight assignment on edges towards improved community detection , 2019, IDEAS.

[2]  Other Contributors Are Indicated Where They Contribute Python Software Foundation , 2017 .

[3]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[4]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[6]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[7]  Katerina Potika,et al.  Community Detection via Neighborhood Overlap and Spanning Tree Computations , 2018, ALGOCLOUD.

[8]  Katerina Potika,et al.  Overlapping Community Detection via Minimum Spanning Tree Computations , 2020, 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService).

[9]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[10]  U. Brandes A faster algorithm for betweenness centrality , 2001 .