Detection of spatiotemporal outlier events in social networks

Didi Surian, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary/reference book

Abstract

In this entry, we extract temporal and spatial outlier events from a large online location-based social network. We analyze the check-in patterns and friendship networks of the users to determine the changes of topic of a location from time to time. A topic of a location at a specific time is determined by check-in patterns and number of friendship networks. Dealing with a worldwide scale check-in patterns introduces a new challenge as the exact location information (e.g., location's specific function, location's exact geo-position) is difficult to determine. To address this issue, we use a unique combination of generative model and von Mises-Fisher distribution to determine the topic of a location at a specific time, given the check-in patterns and friendship networks information of all users who check in. We showcase our proposed approach by performing experiments using dataset (Leskovec 2012) extracted from Brightkite, which was one of the largest location-based social networking services.
Original languageEnglish
Title of host publicationEncyclopedia of social network analysis and mining
EditorsReda Alhajj, Jon Rokne
Place of PublicationNew York
PublisherSpringer, Springer Nature
Pages364-369
Number of pages6
ISBN (Print)9781461461692
DOIs
Publication statusPublished - 2014
Externally publishedYes

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  • Cite this

    Surian, D., & Chawla, S. (2014). Detection of spatiotemporal outlier events in social networks. In R. Alhajj, & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 364-369). New York: Springer, Springer Nature. https://doi.org/10.1007/978-1-4614-6170-8_324