Abstract
Community detection on social networks typically aims to cluster users into different communities based on their social links. The increasing popularity of Location-based Social Networks offers the opportunity to augment these social links with spatial information, for detecting location-centric communities that frequently visit similar places. Such location-centric communities are important to companies for their location-based and mobile advertising efforts. We propose an approach to detect location-centric communities by augmenting social links with both spatial and temporal information, and demonstrate its effectiveness using two Foursquare datasets. In addition, we study the effects of social, spatial and temporal information on communities and observe the following: (i) augmenting social links with spatial and temporal information results in location-centric communities with high levels of check-in and locality similarity; (ii) using spatial and temporal information without social links however leads to communities that are less location-centric.
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Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. of Statistical Mechanics 2008(10), P10008 (2008)
Brown, C., Nicosia, V., et al.: The importance of being placefriends: Discovering location-focused online communities. In: Proc. of WOSN, pp. 31–36 (2012)
Brown, C., Noulas, A., Mascolo, C., Blondel, V.: A place-focused model for social networks in cities. In: Proc. of SocialCom, pp. 75–80 (2013)
Crandall, D.J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. PNAS 107(52) (2010)
Dhar, S., Varshney, U.: Challenges and business models for mobile location-based services and advertising. Communications of the ACM 54(5), 121–128 (2011)
Fortunato, S.: Community detection in graphs. Physics Reports 486(3) (2010)
Gao, H., Tang, J., Liu, H.: Exploring social-historical ties on location-based social networks. In: Proc. of ICWSM, pp. 114–121 (2012)
Gao, H., Tang, J., Liu, H.: gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. In: Proc. of CIKM, pp. 1582–1586 (2012)
Lim, K.H., Datta, A.: Tweets beget propinquity: Detecting highly interactive communities on twitter using tweeting links. In: Proc. of WI-IAT, pp. 214–221 (2012)
Onnela, J.P., Arbesman, S., González, M.C., Barabási, A.L., Christakis, N.A.: Geographic constraints on social network groups. PLoS One 6(4), e16939 (2011)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phy. Review E 76(3), 36106 (2007)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105(4), 1118–1123 (2008)
Sadilek, A., Kautz, H., Bigham, J.P.: Finding your friends and following them to where you are. In: Proc. of WSDM, pp. 723–732 (2012)
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Lim, K.H., Chan, J., Leckie, C., Karunasekera, S. (2015). Detecting Location-Centric Communities Using Social-Spatial Links with Temporal Constraints. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_53
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DOI: https://doi.org/10.1007/978-3-319-16354-3_53
Publisher Name: Springer, Cham
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