Comparison of communities detection algorithms for multiplex

Loe, Chuan Wen; Jensen, Henrik Jeldtoft

doi:10.1016/j.physa.2015.02.089

Cited by 51 publications

(33 citation statements)

References 30 publications

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“…There are, in principle, many ways to define communities in multilayer networks [22,32], but the challenge is to construct an effective framework. The challenge may seem daunting since there is still debate about how to define communities in single-layer networks [10], and multilayer networks are inherently more complex with simultaneous and nonlinear coupling between the layers.…”

Section: B Communities In Multilayer Networkmentioning

confidence: 99%

Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems

et al. 2015

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To comprehend interconnected systems across the social and natural sciences, researchers have developed many powerful methods to identify functional modules. For example, with interaction data aggregated into a single network layer, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of agents or components that exhibit multiple layers of interactions, possibly from several different processes. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here, we propose a method based on a compression of network flows that can identify modular flows both within and across layers in nonaggregated multilayer networks. Our numerical experiments on synthetic multilayer networks, with some layers originating from the same interaction process, show that the analysis fails in aggregated networks or when treating the layers separately, whereas the multilayer method can accurately identify modules across layers that originate from the same interaction process. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks with topics as layers: scientists affiliated with the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method uncovers connected topics and reveals smaller modules with more overlap that better capture the actual organization.

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Section: B Communities In Multilayer Networkmentioning

confidence: 99%

Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems

et al. 2015

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“…The system in which the same nodes belong to multiple networks is typically referred to as a multiplex network or multigraph (see Wasserman (1994), for example). In recent literature, there has been an upsurge of interest in multiplex networks (see, for example, Cozzo et al (2012), Loe and Jeldtoft Jensen (2014), Rank et al (2010), Szell et al (2010), Mucha et al (2010), Maggioni et al (2013), Brummitt et al (2012), Saumell-Mendiola et al (2012), Bianconi (2013) and Nicosia et al (2013)). In these multiplex networks, different kinds of link (or connection) are possible for each pair of nodes.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Block Models for Multiplex Networks: An Application to a Multilevel Network of Researchers

Barbillon

Donnet

Lazega

et al. 2016

Journal of the Royal Statistical Society Series A: Statistics in Society

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Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship. We propose to estimate the parameters-such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups-through a variational expectation-maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possible links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together.

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“…Then, researchers made a comparison of communities' detection algorithms for multiplex that is considered as a set of graphs on the same vertex set because the wrong choice of algorithm can deviate from its intended purpose (Loe & Jensen, 2015). Some studies discuss the topological characteristics of legitimate users, including the formation of tightly knit communities because they consider that it is a promising approach, but they need to devise efficient techniques for identifying hackers, personal attack (FIDIS, 2016) and spammers along with attackers.…”

Section: Related Workmentioning

confidence: 99%

A New Model for Rating Users’ Profiles in Online Social Networks

Malli¹,

Said²,

Fadlallah³

2017

CIS

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Profiling users in Online Social Networks (OSNs) is of great benefit in multiple domains (e.g., marketing, sociology, and forensics). In this paper, we propose a new model for rating user's profile (i.e., low, medium, high, and advanced) in an OSN community by embedding it into clusters located at predefined range of radius in a low-dimensional Cartesian space. The orthogonal coordinates of the profile are estimated using Principle Component Analysis (PCA) applied on a vector of metrics formulated as a set of attributes of interest (i.e., qualitative and quantitative) mined from the user's profile to characterize his/her level of participation and behavior in the community. The experimentations are conducted on 3000 simulated profiles of three OSNs (Facebook, Twitter and Instagram) by embedding them in three Cartesian spaces of three corresponding communities (Religion, Political and Lifestyle). The results show that we are able to estimate accurately the profile rates by reducing the vector of metrics to a low-dimensional space whittle down to 3-D space.

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Comparison of communities detection algorithms for multiplex

Cited by 51 publications

References 30 publications

Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems

Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems

Stochastic Block Models for Multiplex Networks: An Application to a Multilevel Network of Researchers

A New Model for Rating Users’ Profiles in Online Social Networks

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