Abstract
Community structure plays an essential role in analyzing networks. Various algorithms exist to find the community structure that scores high on a graph clustering index called Modularity. In divisive community structure algorithms, initially, all the nodes belong to a single community. Each iteration divides the nodes into two groups, and finally, each node belongs to a single community. The main disadvantage of a divisive algorithm is that it is not able to find whether to divide the community further or not. A divisive community detection algorithm is proposed based on the graph spectra that give the termination method for community detection. We rely on Weighted Spectral Distribution (WSD) to divide the network into small sub-network or not. Experiments with various real-world networks show that the proposed method constantly compares favorably with the popular Girvan Newman’s community detection algorithm.
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11 February 2023
A correction has been published.
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Acknowledgements
This work is supported by Science and Engineering Research Board (SERB), DST, Government of India under MATRICS project (Fill No. MTR/2019/000631).
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Vashishtha, R., Singh, A., Cherifi, H. (2023). A Community Detection Algorithm Using Random Walk. In: Dinh, T.N., Li, M. (eds) Computational Data and Social Networks . CSoNet 2022. Lecture Notes in Computer Science, vol 13831. Springer, Cham. https://doi.org/10.1007/978-3-031-26303-3_20
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