Fast Computation of Modularity in Agglomerative Clustering Methods for Community Discovery

In this paper, we propose a fast method for successive modularity computations in agglomerative clustering for community discovery. Our method is based on a new data structure to maintain two statistical values derived by the definition of modularity between two adjacent (temporary) communities. We prove the correctness and efficiency of the method. Then, we perform it on standard datasets. The experimental results show that our method improves the efficiency significantly.

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