On diameter based community structure identification in networks

Document Type

Conference Article

Publication Title

ACM International Conference Proceeding Series


Several community detection algorithms for large scale networks have been reported in the literature so far. But to the best of our knowledge none of these algorithms consider the diameter of the community as a key parameter. In this paper, we propose a new metric to measure the quality of the community structure identified in a network which is less computation intensive and more realistic. Also, we develop a new heuristic to identify community structures considering the diameter of the community as one of the determinant factors. To ensure good quality of the community structure, we restrict the diameter of a community, upper bounded by a certain value k ∗ D, the diameter of the original network. Comparison with existing algorithms by simulation on well-known graphs such as Zachary karate club, Dolphin network and Football club shows our algorithm performs better in terms of modularity of the community structure achieved. Also, it establishes the fact that the modularity parameter defined by us, can compare the community structures of a given network better.



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