Attributed Networks Partitioning Based on Modularity Optimization

Combe, David A.; Largeron, Christine; Jeudy, Baptiste; Fogelman-Soulié, Françoise; Wang, Jing

doi:10.1002/9781119695110.ch8

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Recently, Hollocou et al (2019) propose a relaxation of the modularity maximization problem which assigns each element of the dataset a probability to belong to a given cluster, whereas a solution of the standard modularity problem is a partition. Furthermore, Combe et al (2020) develop modularity based clustering for complex networks with node attributes. Finally, there are multiple results on modularity optimization using SDP and other relaxation methods in conjunction with SBM (see Section 3).…”

Section: Other Popular Methodsmentioning

confidence: 99%

Community detection in complex networks: From statistical foundations to data science applications

Dey

Tian

Gel

2021

WIREs Computational Stats

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Identifying and tracking community structures in complex networks are one of the cornerstones of network studies, spanning multiple disciplines, from statistics to machine learning to social sciences, and involving even a broader range of application areas, from biology to politics to blockchain. This survey paper aims to provide an overview of some most popular approaches in statistical network community detection as well as the newly emerging research directions such as community extraction with higher‐order features and community discovery in multilayer and multiscale networks. Our goal is to offer a unified view at methodological interconnections and the wide spectrum of interdisciplinary data science applications of network community analysis. This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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Section: Other Popular Methodsmentioning

confidence: 99%