Comparison of Communities Detection Algorithms for Multiplex

Abstract

Multiplex is a set of graphs on the same vertex set, i.e. $\{G(V,E_1),\dots ,G(V,E_m)\}$. It is a generalized graph to model multiple relationships with parallel edges between vertices. This paper is a literature review of existing communities detection algorithms for multiplex and a comparative analysis of them.

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• Community structure in social and biological networks
• Modularity and community structure in networks
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• Multilayer Networks
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• Correlated multiplexity and connectivity of multiplex random networks
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Cited by in corpus (10)

• Hidden geometric correlations in real multiplex networks
• Community Detection and Improved Detectability in Multiplex Networks
• Graph Neural Network for Interpreting Task-fMRI Biomarkers
• Stochastic Block Models for Multiplex networks: an application to networks of researchers
• Principled Multilayer Network Embedding
• Metrics for Community Analysis: A Survey
• A Systematic Survey on Multi-relational Community Detection
• Prime stars multiplexes
• Node-centric community detection in multilayer networks with layer-coverage diversification bias
• A Novel Methodologyof Router-To-ASMapping inspired by Community Discovery