2016
Journal article
Open Access
Homophilic network decomposition: a community-centric analysis of online social services
Rossetti G., Pappalardo L., Kikas R., Pedreschi D., Giannotti F., Dumas M.
Computer Science Applications Classification Complex Networks Media Technology Information Systems Community Discovery Human-Computer Interaction Communication
In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks--Skype, LastFM and Google+--and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.
Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0411-4
Publisher: Springer, Vienna
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McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. In: Annual review of sociology
Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67:026126
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113
Oentaryo RJ, Lim E-P, Lo D, Zhu F, Prasetyo PK (2012) Collective churn prediction in social network. In: ASONAM
Palla G, Dere´nyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nat 435(7043):814-818
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Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. In: PNAS
Tsuruoka Y, Tsujii J, Ananiadou S (2009) Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty. In: ACL/IJCNLP
Wang Y, Zang H, Faloutsos M (2013) Inferring cellular user demographic information using homophily on call graphs. In: 2013 Proceedings IEEE INFOCOM workshops, Turin, Italy, 14-19 Apr 2013, pp 211-216 (Online). doi:10.1109/INFCOMW. 2013.6562897
Watts D, Strogatz S (1998) Collective dynamics of 'small-world' networks. Nature 393:440-442
Yuan G, Murukannaiah PK, Zhang Z, Singh MP (2014) Exploiting sentiment homophily for link prediction. In: Eighth ACM conference on recommender systems, RecSys '14, Foster City, Silicon Valley, CA, 06-10 Oct 2014, pp 17-24 (Online). doi:10. 1145/2645710.2645734
Zardi H, Romdhane LB, Guessoum Z (2014) A multi-agent homophily-based approach for community detection in social networks. In: 26th IEEE international conference on tools with artificial intelligence, ICTAI 2014, Limassol, Cyprus, 10-12 Nov 2014, pp 501-505 (Online). doi:10.1109/ICTAI.2014.81
Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: ICML
Zhao T, Hu J, He P, Fan H, Lyu MR, King I (2014) Exploiting homophily-based implicit social network to improve recommendation performance. In: 2014 International joint conference on neural networks, IJCNN 2014, Beijing, China, 6-11 July 2014, pp 2539-2547. (Online). doi:10.1109/IJCNN.2014.6889743
Zhu Y, Zhong E, Pan SJ, Wang X, Zhou MQY (2013) Predicting user activity level in social networks. In: CIKM
Bessi A, Petroni F, Vicario MD, Zollo F, Anagnostopoulos A, Scala A, Caldarelli G, Quattrociocchi W (2015) Viral misinformation: the role of homophily and polarization. In: Proceedings of the 24th international conference on world wide web companion, WWW 2015, Florence, Italy, May 18-22, 2015-companion volume
Bhatt R, Chaoji V, Parekh R (2010) Predicting product adoption in large-scale social networks. In: CIKM
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008
Carullo G, Castiglione A, Santis AD, Palmieri F (2015) A triadic closure and homophily-based recommendation system for online social networks. World Wide Web 18(6): 1579-1601 (Online). doi:10.1007/s11280-015-0333-5
Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Rev E Phys
Coscia M, Giannotti F, Pedreschi D (2012) A classification for community discovery methods in complex networks. In: CoRR
Coscia M, Rossetti G, Giannotti F, Pedreschi D (2014) Uncovering hierarchical and overlapping communities with a local-first approach. In: TKDD
Domingos P, Richardson M (2001) Mining the network value of customers. In: SIGKDD
Elkabani I, Khachfeh RAA (2015) Homophily-based link prediction in the facebook online social network: a rough sets approach. J Intell Syst 24(4):491-503 (Online). http://www.degruyter.com/view/j/ jisys.2015.24.issue-4/jisys-2014-0031/jisys-2014-0031.xml
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75-174
Fortunato S, Barthe´lemy M (2007) Resolution limit in community detection. IN: PNAS
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. In: PNAS
Gong NZ, Xu W, Huang L, Mittal P, Stefanov E, Sekar V, Song D (2012) Evolution of social-attribute networks: measurements, modeling, and implications using google?. CoRR abs/ 1209.0835 (Online). http://arxiv.org/abs/1209.0835
Hartline JD, Mirrokni VS, Sundararajan M (2008) Optimal marketing strategies over social networks. In: WWW
Himelboim I, McCreery S, Smith M (2013) Birds of a feather tweet together: integrating network and content analyses to examine cross-ideology exposure on twitter. J Comput Med Commun 18(2):40-60
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. In: Annual review of sociology
Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67:026126
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113
Oentaryo RJ, Lim E-P, Lo D, Zhu F, Prasetyo PK (2012) Collective churn prediction in social network. In: ASONAM
Palla G, Dere´nyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nat 435(7043):814-818
Richter Y, Yom-Tov E, Slonim N (2010) Predicting customer churn in mobile networks through analysis of social groups. In: SDM
Rossetti G, Guidotti R, Pennacchioli D, Pedreschi D, Giannotti F (2015) Interaction prediction in dynamic networks exploiting community discovery. In: International conference on advances in social network analysis and mining, IEEE, pp 553-558 (Online). http://dl.acm.org/citation.cfm?doid=2808797.2809401
Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. In: PNAS
Tsuruoka Y, Tsujii J, Ananiadou S (2009) Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty. In: ACL/IJCNLP
Wang Y, Zang H, Faloutsos M (2013) Inferring cellular user demographic information using homophily on call graphs. In: 2013 Proceedings IEEE INFOCOM workshops, Turin, Italy, 14-19 Apr 2013, pp 211-216 (Online). doi:10.1109/INFCOMW. 2013.6562897
Watts D, Strogatz S (1998) Collective dynamics of 'small-world' networks. Nature 393:440-442
Yuan G, Murukannaiah PK, Zhang Z, Singh MP (2014) Exploiting sentiment homophily for link prediction. In: Eighth ACM conference on recommender systems, RecSys '14, Foster City, Silicon Valley, CA, 06-10 Oct 2014, pp 17-24 (Online). doi:10. 1145/2645710.2645734
Zardi H, Romdhane LB, Guessoum Z (2014) A multi-agent homophily-based approach for community detection in social networks. In: 26th IEEE international conference on tools with artificial intelligence, ICTAI 2014, Limassol, Cyprus, 10-12 Nov 2014, pp 501-505 (Online). doi:10.1109/ICTAI.2014.81
Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: ICML
Zhao T, Hu J, He P, Fan H, Lyu MR, King I (2014) Exploiting homophily-based implicit social network to improve recommendation performance. In: 2014 International joint conference on neural networks, IJCNN 2014, Beijing, China, 6-11 July 2014, pp 2539-2547. (Online). doi:10.1109/IJCNN.2014.6889743
Zhu Y, Zhong E, Pan SJ, Wang X, Zhou MQY (2013) Predicting user activity level in social networks. In: CIKM
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BibTeX entry
@article{oai:it.cnr:prodotti:366891,
title = {Homophilic network decomposition: a community-centric analysis of online social services},
author = {Rossetti G. and Pappalardo L. and Kikas R. and Pedreschi D. and Giannotti F. and Dumas M.},
publisher = {Springer, Vienna},
doi = {10.1007/s13278-016-0411-4},
journal = {Social Network Analysis and Mining},
volume = {6},
year = {2016}
}
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