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A modified distance dynamics model for improvement of community detection

Citation

Meng, T and Cai, L and He, T and Chen, L and Deng, Z and Ding, W and Cao, Z, A modified distance dynamics model for improvement of community detection, IEEE Access, 6 pp. 63934-63947. ISSN 2169-3536 (2018) [Refereed Article]


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Copyright Statement

Copyright 2018 IEEE.

DOI: doi:10.1109/ACCESS.2018.2877235

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 Details

Item Type:Refereed Article
Keywords:community detection, complex network, distance dynamics model, membership function, data
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Pattern Recognition and Data Mining
Objective Division:Manufacturing
Objective Group:Computer Hardware and Electronic Equipment
Objective Field:Integrated Systems
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:131575
Year Published:2018
Deposited By:Information and Communication Technology
Deposited On:2019-03-23
Last Modified:2019-05-13
Downloads:5 View Download Statistics

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