Mining for spatially-near communities in geo-located social networks

Joseph Hannigan, Guillermo Hernandez, Richard M. Medina, Patrick Roos, Paulo Shakarian

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

7 Scopus citations


Current approaches to community detection in social networks often ignore the spatial location of the nodes. In this paper, we look to extract spatially-near communities in a social network. We introduce a new metric to measure the quality of a community partition in a geolocated social networks called "spatially-near modularity" a value that increases based on aspects of the network structure but decreases based on the distance between nodes in the communities. We then look to find an optimal partition with respect to this measure - which should be an "ideal" community with respect to both social ties and geographic location. Though an NP-hard problem, we introduce two heuristic algorithms that attempt to maximize this measure and outperform non-geographic community finding by an order of magnitude. Applications to counter-terrorism are also discussed.

Original languageEnglish (US)
Title of host publicationSocial Networks and Social Contagion
Subtitle of host publicationWeb Analytics and Computational Social Science - Papers from the AAAI Fall Symposium, Technical Report
PublisherAI Access Foundation
Number of pages8
ISBN (Print)9781577356431
StatePublished - 2013
Externally publishedYes
Event2013 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 15 2013Nov 17 2013

Publication series

NameAAAI Fall Symposium - Technical Report


Other2013 AAAI Fall Symposium
Country/TerritoryUnited States
CityArlington, VA

ASJC Scopus subject areas

  • General Engineering


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