Communities and beyond: Mesoscopic analysis of a large social network with complementary methods

Tibély, Gergely; Kovanen, Lauri; Karsai, Márton; Kaski, Kimmo; Kertész, János; Saramäki, Jari

doi:10.1103/physreve.83.056125

Cited by 26 publications

(21 citation statements)

References 31 publications

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“…This way, we adopt a covering-like approach similar to the one used in [30]. Given a partition P i on V i and a community c i ∈ P i , we compute how it is covered by the communities of another partition P j on V j .…”

Section: Communities Match Methodsmentioning

confidence: 99%

Calling, texting, and moving: multidimensional interactions of mobile phone users

Zignani

Quadri

Gaito

et al. 2015

Compu Social Networls

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The communication networks obtained by using mobile phone datasets have drawn increasing attention in recent years. Studies have led to important advances in understanding the behavior of mobile users although they have just considered text message (short message service (SMS)), call data, and spatial proximity, separately. However, there is a growing awareness that human sociality is expressed simultaneously on multiple layers, each corresponding to a specific way an individual has to communicate. In fact, besides the common real life encounters, a mobile phone user has at least two further communication media to exploit, SMSs and voice calls. This is advocating a multidimensional approach if we are seeking a compound description of the human mobile social behavior. In this context, we perform the first study of the multiplex mobile network, gathered from the records of both call and text message activities, along with relevant geographical information, of millions of users of a large mobile phone operator over a period of 12 weeks. By computing a set of complex network metrics, at different scales, onto the three single layers given by calls, SMSs and spatial proximity, and their extensions onto a three-level network, we provide a comprehensive study of the global multi-layered network which arises from both the overall on-the-phone communications performed by mobile users and their spatial propinquity.

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Section: Communities Match Methodsmentioning

confidence: 99%

Calling, texting, and moving: multidimensional interactions of mobile phone users

Zignani

Quadri

Gaito

et al. 2015

Compu Social Networls

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“…Moreover, the particular structure of mobile call graphs induces some issues for traditional community detection methods. Tibely et al [24] show that even though some community detection methods perform well on benchmark networks, they do not produce clear community structures on mobile call graphs. Mobile call graphs contain many small tree-like structures, which are badly handled by most community detection methods.…”

Section: Communitiesmentioning

confidence: 99%

A survey of results on mobile phone datasets analysis

2015

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In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.

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“…Both methods are available in NetworkChange. Tibély et al, 2011;Sporns, 2014). Technically, various approaches for mesoscopic trait discovery locates nodes in a discrete or continuous latent space on the basis of their similarity.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Detecting Structural Changes in Longitudinal Network Data

Park¹,

Sohn²

2020

Bayesian Anal.

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Dynamic modeling of longitudinal networks has been an increasingly important topic in applied research. While longitudinal network data commonly exhibit dramatic changes in its structures, existing methods have largely focused on modeling smooth topological changes over time. In this paper, we develop a hidden Markov multilinear tensor model (HMTM) that combines the multilinear tensor regression model (Hoff, 2011) with a hidden Markov model using Bayesian inference. We model changes in network structure as shifts in discrete states yielding particular sets of network generating parameters. Our simulation results demonstrate that the proposed method correctly detects the number, locations, and types of changes in latent node characteristics. We apply the proposed method to international military alliance networks to find structural changes in the coalition structure among nations. arXiv:1811.01384v1 [stat.ME] 4 Nov 2018 1. group-structured networks with no break (Table 1)

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Communities and beyond: Mesoscopic analysis of a large social network with complementary methods

Cited by 26 publications

References 31 publications

Calling, texting, and moving: multidimensional interactions of mobile phone users

Calling, texting, and moving: multidimensional interactions of mobile phone users

A survey of results on mobile phone datasets analysis

Detecting Structural Changes in Longitudinal Network Data

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