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A modified distance dynamics model for improvement of community detection

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Meng, T, Cai, L, He, T, Chen, L, Deng, Z, Ding, W and Cao, Z ORCID: 0000-0003-3656-0328 2018 , 'A modified distance dynamics model for improvement of community detection' , IEEE Access, vol. 6 , pp. 63934-63947 , doi: 10.1109/ACCESS.2018.2877235.

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Abstract

Community detection is a key technique for identifying the intrinsic community structures of complex networks. The distance dynamics model has been proven effective in finding communities with arbitrary size and shape and identifying outliers. However, to simulate distance dynamics, the model requires manual parameter specification and is sensitive to the cohesion threshold parameter, which is difficult to determine. Furthermore, it has difficulty handling rough outliers and ignores hubs (nodes that bridge communities). In this paper, we propose a robust distance dynamics model, namely, Attractor++, which uses a dynamic membership degree. In Attractor++, the dynamic membership degree is used to determine the influence of exclusive neighbors on the distance instead of setting the cohesion threshold. In addition, considering its inefficiency and low accuracy in handling outliers and identifying hubs, we design an outlier optimization model that is based on triangle adjacency. By using optimization rules, a postprocessing method further judges whether a singleton node should be merged into the same community as its triangles or regarded as a hub or an outlier. Extensive experiments on both real-world and synthetic networks demonstrate that our algorithm more accurately identifies nodes that have special roles (hubs and outliers) and more effectively identifies community structures.

Item Type: Article
Authors/Creators:Meng, T and Cai, L and He, T and Chen, L and Deng, Z and Ding, W and Cao, Z
Keywords: community detection, complex network, distance dynamics model, membership function, data
Journal or Publication Title: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2169-3536
DOI / ID Number: 10.1109/ACCESS.2018.2877235
Copyright Information:

Copyright 2018 IEEE.

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