Visible to the public Spammer Detection for Real-time Big Data Graphs

TitleSpammer Detection for Real-time Big Data Graphs
Publication TypeConference Paper
Year of Publication2016
AuthorsEom, Chris Soo-Hyun, Lee, Wookey, Lee, James Jung-Hun
Conference NameProceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory
Date PublishedOctober 2016
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4754-9
Keywordsbig data privacy, circuit, composability, compositionality, graph, Human Behavior, local clustering coefficient, pubcrawl, Resiliency, Scalability, shortest path, spam detection, spammer

In recent years, prodigious explosion of social network services may trigger new business models. However, it has negative aspects such as personal information spill or spamming, as well. Amongst conventional spam detection approaches, the studies which are based on vertex degrees or Local Clustering Coefficient have been caused false positive results so that normal vertices can be specified as spammers. In this paper, we propose a novel approach by employing the circuit structure in the social networks, which demonstrates the advantages of our work through the experiment.

Citation Keyeom_spammer_2016