See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/278968259 Collective Spammer Detection in Evolving Multi-Relational Social Networks Conference Paper · August 2015 DOI: 10.1145/2783258.2788606 CITATIONS READS 17 282 4 authors, including: Shobeir Fakhraei Lise Getoor University of Southern California University of Maryland, College Park 16 PUBLICATIONS 169 CITATIONS 238 PUBLICATIONS 9,126 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Medical Decision Making for individual patients View project All content following this page was uploaded by Shobeir Fakhraei on 26 June 2015. The user has requested enhancement of the downloaded file. Collective Spammer Detection in Evolving Multi-Relational Social Networks Shobeir Fakhraei James Foulds Madhusudana Shashanka ∗ y University of Maryland University of California Santa Cruz, CA, USA if(we) Inc. College Park, MD, USA San Francisco, CA, USA
[email protected] [email protected] [email protected] Lise Getoor University of California Santa Cruz, CA, USA
[email protected] ABSTRACT Keywords Detecting unsolicited content and the spammers who create Social Networks, Spam, Social Spam, Collective Classifica- it is a long-standing challenge that affects all of us on a daily tion, Graph Mining, Multi-relational Networks, Heteroge- basis. The recent growth of richly-structured social net- neous Networks, Sequence Mining, Tree-Augmented Naive works has provided new challenges and opportunities in the Bayes, k-grams, Hinge-loss Markov Random Fields (HL- spam detection landscape. Motivated by the Tagged.com1 MRFs), Probabilistic Soft Logic (PSL), Graphlab. social network, we develop methods to identify spammers in evolving multi-relational social networks.