Homophilic network decomposition: a community-centric analysis of online social services

被引：3

作者：

Rossetti G. ^{[1
,2
]}

Pappalardo L. ^{[2
]}

Kikas R. ^{[3
]}

Pedreschi D. ^{[2
]}

Giannotti F. ^{[1
]}

Dumas M. ^{[3
]}

机构：

[1] KDDLab, ISTI -CNR, Via G. Moruzzi, 1, Pisa

[2] KDDLab, University of Pisa, Largo B. Pontecorvo, 3, Pisa

[3] Unversity of Tartu, Tartu

来源：

Social Network Analysis and Mining | 2016年 / 6卷 / 1期

基金：

欧盟地平线“2020”;

关键词：

Online Social Network; Minority Class; Stochastic Gradient Descent; Community Detection Algorithm; User Engagement;

D O I：

10.1007/s13278-016-0411-4

中图分类号：

学科分类号：

摘要：

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+. © 2016, The Author(s).

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