Detecting Overlapping Community Structures with PCA Technology and Member Index

Peiyan Yuan; Wei Wang; Mingyang Song

The 3rd International Workshop on Data, Text, Web, and Social Network Mining

Research Article

Detecting Overlapping Community Structures with PCA Technology and Member Index

Cite: BibTeX Plain Text

@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

Peiyan Yuan¹^,*, Wei Wang¹, Mingyang Song²

1: School of Computer and Information Engineering, Henan Normal University
2: School of Computer and Information Engineering Henan Normal University

*Contact email: peiyan@htu.cn

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.

Keywords: social networks overlapping community structure principle component analysis membership index

Published: 2016-12-12
Publisher: ACM

: http://dx.doi.org/10.4108/eai.18-6-2016.2264206