Supervised community detection in multiplex networks based on layers convex flattening and modularity optimization

Gurov, Andrei; Evmenova, Elizaveta; Chunaev, Petr

doi:10.1016/j.procs.2022.11.002

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…We compared the accuracy of 17 community detection algorithms along with our proposed CE-OR aggregation algorithm over real world and synthetic data sets which have ground truth community structure available. The algorithms used are: flat_nw and flat_ec (Berlingerio et al, 2011 ), abacus (Berlingerio et al, 2013 ), cpm (Afsarmanesh and Magnani, 2016 ), glouvain (Mucha et al, 2010 ), infomap (De Domenico et al, 2015 ), scml (Dong et al, 2013 ), pmm (Tang et al, 2009 ) (Tang et al, 2012 ), lart (Kuncheva and Montana, 2015 ), emcd (Tagarelli et al, 2017 ), mlp (Boutemine and Bouguessa, 2017 ), and multiplex-leiden (Gurov et al, 2022 ). The results show that on an average the CE-OR (Metric-Aggregate) algorithm has 89% accuracy and CE-OR (Metric-Fraction) algorithm has 82% accuracy , which is significantly higher than the other methods.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The bayesian model has been found to effectively identify community structure from a multiplex network in Amini et al (2022). A supervised algorithm based on layer convex flattening and modularity optimization of the network, as shown in Gurov et al (2022), has been developed for community detection in multiplex networks. Bio-inspired optimization has successfully solved the problem of community detection in network that we observe from the work Osaba et al (2020) where the authors present in detail the problem of community detection from view of bio-inspired computation.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Efficient community detection in multilayer networks using boolean compositions

Santra,

Irany,

Madduri

et al. 2023

Front. Big Data

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Networks (or graphs) are used to model the dyadic relations between entities in complex systems. Analyzing the properties of the networks reveal important characteristics of the underlying system. However, in many disciplines, including social sciences, bioinformatics, and technological systems, multiple relations exist between entities. In such cases, a simple graph is not sufficient to model these multiple relations, and a multilayer network is a more appropriate model. In this paper, we explore community detection in multilayer networks. Specifically, we propose a novel network decoupling strategy for efficiently combining the communities in the different layers using the Boolean primitives AND, OR, and NOT. Our proposed method, network decoupling, is based on analyzing the communities in each network layer individually and then aggregating the analysis results. We (i) describe our network decoupling algorithms for finding communities, (ii) present how network decoupling can be used to express different types of communities in multilayer networks, and (iii) demonstrate the effectiveness of using network decoupling for detecting communities in real-world and synthetic data sets. Compared to other algorithms for detecting communities in multilayer networks, our proposed network decoupling method requires significantly lower computation time while producing results of high accuracy. Based on these results, we anticipate that our proposed network decoupling technique will enable a more detailed analysis of multilayer networks in an efficient manner.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Efficient community detection in multilayer networks using boolean compositions

Santra,

Irany,

Madduri

et al. 2023

Front. Big Data

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Bi-objective model for community detection in weighted complex networks

Torres-Cockrell,

Mora-Gutiérrez,

Rincón-García

et al. 2024

EAI Endorsed Trans Ind Net Intel Syst

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In this study, we introduce an innovative approach that utilizes complex networks and the k_core method to address community detection in weighted networks. Our proposed bi-objective model aims to simultaneously discover non-overlapping communities while ensuring that the degree of similarity remains below a critical threshold to prevent network degradation. We leverage the k_core structure to detect tightly interconnected node groups, a concept particularly valuable in edge-weighted networks where different edge weights indicate the strength or importance of node relationships. Beyond maximizing the count of k_core communities, our model seeks a homogeneous weight distribution across edges within these communities, promoting stronger cohesion. To tackle this challenge, we implement two multi-target algorithms: Non-dominated Sorting Genetic Algorithm II (NSGAII) and a Multi-Objective Simulated Annealing (MOSA) algorithm. Both algorithms efficiently identify non-overlapping communities with a specified degree 'k'. The results of our experiments reveal a trade-off between maximizing the number of k_core communities and enhancing the homogeneity of these communities in terms of their minimum weighted interconnections. Notably, the MOSA algorithm outperforms NSGAII in both small and large instances, demonstrating its effectiveness in achieving this balance. This approach sheds light on effective strategies for resolving conflicting goals in community detection within weighted networks.

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Supervised community detection in multiplex networks based on layers convex flattening and modularity optimization

Cited by 2 publications

References 23 publications

Efficient community detection in multilayer networks using boolean compositions

Efficient community detection in multilayer networks using boolean compositions

Bi-objective model for community detection in weighted complex networks

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