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Community-centric analysis of user engagement in Skype social network

Published: 25 August 2015 Publication History

Abstract

Traditional approaches to user engagement analysis focus on individual users. In this paper we address user engagement analysis at the level of groups of users (social communities). From the entire Skype social network we extract communities by means of representative community detection methods each one providing node partitions having their own peculiarities. We then examine user engagement in the extracted communities putting into evidence clear relations between topological and geographic features of communities and their mean user engagement. In particular we show that user engagement can be to a great extent predicted from such features. Moreover, from the analysis it clearly emerges that the choice of community definition and granularity deeply affect the predictive performance.

References

[1]
A. Bagherjeiran and R. Parekh, "Combining behavioral and social network data for online advertising." in ICDM Workshops, 2008.
[2]
R. Bhatt, V. Chaoji, and R. Parekh, "Predicting product adoption in large-scale social networks." in CIKM, 2010.
[3]
V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, "Fast unfolding of communities in large networks," Journal of Statistical Mechanics: Theory and Experiment, 2008.
[4]
A. Clauset, M. E. J. Newman, and C. Moore, "Finding community structure in very large networks," Phys. Rev. E, 2004.
[5]
M. Coscia, G. Rossetti, F. Giannotti, and D. Pedreschi, "Uncovering hierarchical and overlapping communities with a local-first approach." TKDD, 2014.
[6]
M. Coscia, F. Giannotti, and D. Pedreschi, "A classification for community discovery methods in complex networks," CoRR, 2012.
[7]
P. Domingos and M. Richardson, "Mining the network value of customers," in SIGKDD, 2001.
[8]
S. Fortunato and M. Barthélemy, "Resolution limit in community detection," PNAS, 2007.
[9]
J. D. Hartline, V. S. Mirrokni, and M. Sundararajan, "Optimal marketing strategies over social networks." in WWW, 2008.
[10]
I. Himelboim, S. McCreery, and M. Smith, "Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on twitter," Journal of Computer-Mediated Communication, 2013.
[11]
M. McPherson, L. Smith-Lovin, and J. M. Cook, "Birds of a feather: Homophily in social networks," Annual Review of Sociology, 2001.
[12]
M. E. J. Newman, "Mixing patterns in networks," Phys. Rev. E, vol. 67, p. 026126, 2003.
[13]
M. E. J. Newman and M. Girvan, "Finding and evaluating community structure in networks," Phys. Rev. E, 2004.
[14]
R. J. Oentaryo, E.-P. Lim, D. Lo, F. Zhu, and P. K. Prasetyo, "Collective churn prediction in social network." in ASONAM, 2012.
[15]
G. Palla, I. Derényi, I. Farkas, and T. Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society," Nature, 2005.
[16]
Y. Richter, E. Yom-Tov, and N. Slonim, "Predicting customer churn in mobile networks through analysis of social groups," in SDM, 2010.
[17]
M. Rosvall and C. T. Bergstrom, "Maps of random walks on complex networks reveal community structure," PNAS, 2008.
[18]
Y. Tsuruoka, J. Tsujii, and S. Ananiadou, "Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty." in ACL/IJCNLP, 2009.
[19]
T. Zhang, "Solving large scale linear prediction problems using stochastic gradient descent algorithms." in ICML, 2004.
[20]
Y. Zhu, E. Zhong, S. J. Pan, X. Wang, M. Zhou, and Q. Y. 0001, "Predicting user activity level in social networks." in CIKM, 2013.

Cited By

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  • (2022)Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3199494(1-14)Online publication date: 2022
  • (2021)TCD2: Tree-based community detection in dynamic social networksExpert Systems with Applications10.1016/j.eswa.2020.114493169(114493)Online publication date: May-2021
  • (2020)Anchored Vertex Exploration for Community Engagement in Social Networks2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00042(409-420)Online publication date: Apr-2020
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cover image ACM Conferences
ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
August 2015
835 pages
ISBN:9781450338547
DOI:10.1145/2808797
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2015

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Cited By

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  • (2022)Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3199494(1-14)Online publication date: 2022
  • (2021)TCD2: Tree-based community detection in dynamic social networksExpert Systems with Applications10.1016/j.eswa.2020.114493169(114493)Online publication date: May-2021
  • (2020)Anchored Vertex Exploration for Community Engagement in Social Networks2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00042(409-420)Online publication date: Apr-2020
  • (2020)ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networksApplied Network Science10.1007/s41109-020-00270-65:1Online publication date: 10-Jun-2020
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  • (2019)Exorcising the Demon: Angel, Efficient Node-Centric Community DiscoveryComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_13(152-163)Online publication date: 26-Nov-2019
  • (2018)DelurkingMining Lurkers in Online Social Networks10.1007/978-3-030-00229-9_6(47-65)Online publication date: 10-Nov-2018
  • (2017)Crisis Management Using Centrality Measurement in Social NetworksInternational Journal of Mobile Computing and Multimedia Communications10.4018/IJMCMC.20170101028:1(19-33)Online publication date: Jan-2017
  • (2017)TilesMachine Language10.1007/s10994-016-5582-8106:8(1213-1241)Online publication date: 1-Aug-2017
  • (2016)A Novel Approach to Evaluate Community Detection Algorithms on Ground TruthComplex Networks VII10.1007/978-3-319-30569-1_10(133-144)Online publication date: 3-Mar-2016

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