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

2 Citations (Scopus)

Abstract

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 publicationAAAI Fall Symposium - Technical Report
PublisherAI Access Foundation
Pages16-23
Number of pages8
VolumeFS-13-05
ISBN (Print)9781577356431
StatePublished - 2013
Externally publishedYes
Event2013 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 15 2013Nov 17 2013

Other

Other2013 AAAI Fall Symposium
CountryUnited States
CityArlington, VA
Period11/15/1311/17/13

Fingerprint

Terrorism
Heuristic algorithms
Computational complexity

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Hannigan, J., Hernandez, G., Medina, R. M., Roos, P., & Shakarian, P. (2013). Mining for spatially-near communities in geo-located social networks. In AAAI Fall Symposium - Technical Report (Vol. FS-13-05, pp. 16-23). AI Access Foundation.

Mining for spatially-near communities in geo-located social networks. / Hannigan, Joseph; Hernandez, Guillermo; Medina, Richard M.; Roos, Patrick; Shakarian, Paulo.

AAAI Fall Symposium - Technical Report. Vol. FS-13-05 AI Access Foundation, 2013. p. 16-23.

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

Hannigan, J, Hernandez, G, Medina, RM, Roos, P & Shakarian, P 2013, Mining for spatially-near communities in geo-located social networks. in AAAI Fall Symposium - Technical Report. vol. FS-13-05, AI Access Foundation, pp. 16-23, 2013 AAAI Fall Symposium, Arlington, VA, United States, 11/15/13.
Hannigan J, Hernandez G, Medina RM, Roos P, Shakarian P. Mining for spatially-near communities in geo-located social networks. In AAAI Fall Symposium - Technical Report. Vol. FS-13-05. AI Access Foundation. 2013. p. 16-23
Hannigan, Joseph ; Hernandez, Guillermo ; Medina, Richard M. ; Roos, Patrick ; Shakarian, Paulo. / Mining for spatially-near communities in geo-located social networks. AAAI Fall Symposium - Technical Report. Vol. FS-13-05 AI Access Foundation, 2013. pp. 16-23
@inproceedings{f0e7f5204b87442881aa27067f5ad950,
title = "Mining for spatially-near communities in geo-located social networks",
abstract = "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.",
author = "Joseph Hannigan and Guillermo Hernandez and Medina, {Richard M.} and Patrick Roos and Paulo Shakarian",
year = "2013",
language = "English (US)",
isbn = "9781577356431",
volume = "FS-13-05",
pages = "16--23",
booktitle = "AAAI Fall Symposium - Technical Report",
publisher = "AI Access Foundation",

}

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

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

SN - 9781577356431

VL - FS-13-05

SP - 16

EP - 23

BT - AAAI Fall Symposium - Technical Report

PB - AI Access Foundation

ER -