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

Abstract

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?.

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

“…Rossetti et al [23] (2016) investigated the relationship between topological features of various networks and the degree of homophily between their subscribers. Skype, Last FM and Google+ users were inspected to determine similarity in usage pattern, listening activity and education respectively.…”

How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science

“…DEMON leverages the nodes perspective to identify meaningful network substructures: it works by identify local-communities at the ego-network level exploiting label propagation and then merging them in an incremental fashion. Our approach has been used as a proxy for users homophily to support network quantification tasks [55]; as filter to reduce the computational cost of Link Prediction approaches [70]; as well as to bound set of Skype users while searching a network driven methodology to relate service usage to network position [71]. Moreover, in order to cope with the evolving nature of interaction networks, we proposed an online dynamic community discovery algorithm, TILES [72], able to track community life cycles as new perturbations appears in the network (i.e.…”