Statistical analysis and anomaly detection of SMS social networks

Bin Zhang, Liye Ma, Ramayya Krishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social network analysis has attracted intensive interests by researchers from multiple disciplines. However most of the existing work is descriptive nature, and statistical network analysis remains an active area of research. In this paper, we model and study two facets of the social networks in short message services (SMS). One is the structure of the contact networks of mobile users, the other is users' messaging behavior pattern. We want to account for the heterogeneity in behavior so that to identify abusive usage such as spamming through the study. We use power-law mixture model to capture community formation behaviors, the first facet, and use Poisson-panel mixture models to uncover abnormal behaviors in text messaging. Our results show heterogeneity of the consumers' sending behavior, also there are two major types of community formation behavior in SMS network.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information Systems 2011, ICIS 2011
Pages3007-3015
Number of pages9
Volume4
Publication statusPublished - 2011
Externally publishedYes
Event32nd International Conference on Information System 2011, ICIS 2011 - Shanghai, China
Duration: Dec 4 2011Dec 7 2011

Other

Other32nd International Conference on Information System 2011, ICIS 2011
CountryChina
CityShanghai
Period12/4/1112/7/11

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Keywords

  • Anomaly detection
  • Parameter estimation
  • Power-law
  • Social network

ASJC Scopus subject areas

  • Information Systems

Cite this

Zhang, B., Ma, L., & Krishnan, R. (2011). Statistical analysis and anomaly detection of SMS social networks. In International Conference on Information Systems 2011, ICIS 2011 (Vol. 4, pp. 3007-3015)