TY - GEN
T1 - Mining for spatially-near communities in geo-located social networks
AU - Hannigan, Joseph
AU - Hernandez, Guillermo
AU - Medina, Richard M.
AU - Roos, Patrick
AU - Shakarian, Paulo
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84898910453&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898910453&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84898910453
SN - 9781577356431
T3 - AAAI Fall Symposium - Technical Report
SP - 16
EP - 23
BT - Social Networks and Social Contagion
PB - AI Access Foundation
T2 - 2013 AAAI Fall Symposium
Y2 - 15 November 2013 through 17 November 2013
ER -