Abstract
Since the relation is the main data shape of social networks, social spammer detection desperately needs a relation-dependent but content-independent framework. Some recent detection method transforms the social relations into a set of topological features, such as degree, k-core, etc. However, the multiple heterogeneous relations and the direction within each relation have not been fully explored for identifying social spammers. In this paper, we make an attempt to adopt the Multi-Relational Embedding (MRE) approach for learning latent features of the social network. The MRE model is able to fuse multiple kinds of different relations and also learn two latent vectors for each relation indicating both sending role and receiving role of every user, respectively. Experimental results on a real-world multi-relational social network demonstrate the latent features extracted by our MRE model can improve the detection performance remarkably.
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Acknowledgment
This work was supported in part by National Key Research and Development Program of China under Grant 2016YFB1000901, the National Natural Science Foundation of China (NSFC) under Grant 71571093, Grant 91646204, Grant 71372188, Grant 71701089, and the National Center for International Joint Research on E-Business Information Processing under Grant 2013B01035.
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Yin, J., Zhou, Z., Liu, S., Wu, Z., Xu, G. (2018). Social Spammer Detection: A Multi-Relational Embedding Approach. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_49
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