Open Access

ARTICLE

Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media

by Chengzhang Zhu, Wentao Zhao, Qian Li, Pan Li, Qiaobo Da

1 Faculty of Engineering and Information Technologies, University of Technology Sydney, 15 Broadway, Ultimo NSW 2007, Australia.
2 College of Computer, National University of Defense Technology, Changsha, China.
3 School of Economics and Management, Beijing Institute of Graphic Communication, Beijing, China.

* Corresponding Author: Wentao Zhao. Email: .

Computers, Materials & Continua 2019, 60(1), 317-333. https://doi.org/10.32604/cmc.2019.05677

Abstract

Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to address this issue. NEST detects multiple groups of collaborative fraudsters by two steps. In the first step, to disclose user collaboration, it represents users according to their social relations. Then, in the second step, to identify the collaborative fraudsters, it detects the user groups with anomalous large group density in its representation space. BEST instantiates NEST by using a bipartite network embedding method to represent users and adopting a fast density group detection method based on the k-dimensional tree. Our experiments show BEST (i) performs significantly better in detecting fraudsters on four real-word social media data sets, and (ii) effectively detects multiple groups of collaborative fraudsters, compared to three state-of-the-art competitors.

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Citations