Research Article
Detecting Overlapping Community Structures with PCA Technology and Member Index
@INPROCEEDINGS{10.4108/eai.18-6-2016.2264206, author={Peiyan Yuan and Wei Wang and Mingyang Song}, title={Detecting Overlapping Community Structures with PCA Technology and Member Index }, proceedings={The 3rd International Workshop on Data, Text, Web, and Social Network Mining}, publisher={ACM}, proceedings_a={DTWSM}, year={2016}, month={12}, keywords={social networks overlapping community structure principle component analysis membership index}, doi={10.4108/eai.18-6-2016.2264206} }- Peiyan Yuan
Wei Wang
Mingyang Song
Year: 2016
Detecting Overlapping Community Structures with PCA Technology and Member Index
DTWSM
ACM
DOI: 10.4108/eai.18-6-2016.2264206
Abstract
The community structures reflect the basic property of social networks and the key point is to detect them effectively. Traditional solutions such as the nonnegative matrix factorization approach have a high time and space complexity, resulting in poor scalability. In this paper, we propose PCA-MI, a novel method to detect the overlapping community structures. Firstly, we use principle component analysis (PCA) technology to extract the key features of network information, and then employ membership index (MI) to classify nodes. Experimental results show that our approach can fast identify the overlapping community structures and achieve approximate Module-Q value as the traditional algorithms.
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