|Title of host publication||Encyclopedia of social network analysis and mining|
|Editors||Reda Alhajj, Jon Rokne|
|Place of Publication||New York|
|Publisher||Springer, Springer Nature|
|Number of pages||6|
|Publication status||Published - 2014|
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.
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