Community detection in networks with unequal groups

Zhang, Pan; Moore, Cristopher; Newman, M. E. J.

doi:10.1103/physreve.93.012303

Cited by 45 publications

(32 citation statements)

References 28 publications

(63 reference statements)

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“…If communities have unequal sizes and the degree of vertices are correlated with the size of their communities, so that vertices have larger degree, the bigger their clusters, community detection becomes easier, as the degrees can be used as proxy for group membership. In this case, the nontrivial detectability limit disappears when there are four clusters or fewer, while it persists up to a given extent of group size inequality when there are more than four clusters (Zhang et al, 2016). Other types of block structure, like core-periphery, do not suffer from detectability issues .…”

Section: Detectabilitymentioning

confidence: 93%

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Community detection in networks: A user guide

2016

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Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.

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Section: Detectabilitymentioning

confidence: 93%

“…LFR benchmark graphs are more complex models than the one studied in (Zhang et al, 2016) and it is not clear whether there is a non-trivial detectability limit, though it is unlikely, due to the big heterogeneity in the distribution of vertex degree and community size.…”

Section: Detectabilitymentioning

confidence: 99%

Community detection in networks: A user guide

2016

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“…There is currently an effort among network theorists to provide a more general framework for studying modular networks (Fortunato ), particularly using generalized community models (Newman & Peixoto ; Zhang et al . ). Such models, adapted to the population‐genetic context, may be used in the future to ask questions regarding the formation of complex modular structures through evolutionary dynamics, or about processes in modular habitat patch networks, such as spread of alleles or local adaptation.…”

Section: Addressing Evolutionary Dynamics In Discrete Habitats Using mentioning

confidence: 97%

“…While the rather limited utilization of community procedures in molecular ecology has so far been mostly focused on revealing hierarchical structures in empirically sampled populations, theoretical studies aimed at understanding evolutionary dynamics in discrete habitat patches may benefit from adopting a network approach as well. There is currently an effort among network theorists to provide a more general framework for studying modular networks (Fortunato 2010), particularly using generalized community models (Newman & Peixoto 2015;Zhang et al 2016). Such models, adapted to the population-genetic context, may be used in the future to ask questions regarding the formation of complex modular structures through evolutionary dynamics, or about processes in modular habitat patch networks, such as spread of alleles or local adaptation.…”

mentioning

confidence: 99%

Application of network methods for understanding evolutionary dynamics in discrete habitats

Greenbaum

Fefferman

2017

Molecular Ecology

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In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory -a branch of discrete mathematics concerned with complex interactions between discrete elements -has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.

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“…Viral marketing has the same objective, since locating the right community is the first step toward effective viral marketing. However, Most of existing methods perform community detection at random basis [53]. In this section, a review of existing community detection methods and models that have been applied to online social networks will be presented to emphasize the importance and current lack of applying community detection to viral marketing.…”

Section: ) Clusteringmentioning

confidence: 99%

Toward Information Diffusion Model for Viral Marketing in Business

AlSuwaidan¹,

Ykhlef²

2016

ijacsa

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Abstract-Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims to investigate the constellation of diffusion methods for viral marketing. Studies on diffusion methods for viral marketing have applied different computational methods, but a systematic investigation of these methods has limited. Most of the literature have focused on achieving objectives such as influence maximization or community detection. Therefore, this article aims to conduct an in-depth review of works related to diffusion for viral marketing. Viral marketing has applied to business-to-consumer transactions but has seen limited adoption in business-to-business transactions. The literature review reveals a lack of new diffusion methods, especially in dynamic and large-scale networks. It also offers insights into applying various mining methods for viral marketing. It discusses some of the challenges, limitations, and future research directions of information diffusion for viral marketing. The article also introduces a viral marketing information diffusion model. The proposed model attempts to solve the dynamicity and large-scale data of social networks by adopting incremental clustering and a stochastic differential equation for business-to-business transactions.

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Community detection in networks with unequal groups

Cited by 45 publications

References 28 publications

Community detection in networks: A user guide

Community detection in networks: A user guide

Application of network methods for understanding evolutionary dynamics in discrete habitats

Toward Information Diffusion Model for Viral Marketing in Business

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