Robust Community Detection in Graphs

Al-sharoa, Esraa; Ababneh, Bara' M.; Al-khassaweneh, Mahmood

doi:10.1109/access.2021.3105692

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…This division aims to reveal the underlying structure of the graph by grouping nodes that share similar characteristics or functions [1,2]. In this study, we address the critical necessity for efficient graph partitioning [3,4] and community detection algorithms [5] to unravel the structural intricacies of large-scale networks spanning diverse domains such as social sciences [6], biology [7], and computer science [8]. To tackle this challenge, we introduce LouvainSplit, a tailored algorithm designed explicitly for this purpose.…”

Section: -Introductionmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

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Graph partitioning is essential for uncovering cohesive communities within complex networks. This paper introduces LouvainSplit, an innovative algorithm designed to enhance graph partitioning efficiency and accuracy. LouvainSplit leverages advanced techniques in feature representation, community detection, and evaluation, providing a robust framework for addressing challenges inherent in graph partitioning tasks across diverse domains. At its core, LouvainSplit utilizes a feature pyramid representation approach to extract both basic and summary features from input graphs at multiple granularity levels. This methodology ensures a subtle evaluation of graph data by capturing fundamental graph information alongside intricate structural patterns, thus offering a comprehensive representation of underlying community structures. A key innovation of our approach is the integration of the Louvain algorithm, renowned for its efficacy in community detection. By leveraging pairwise cosine similarities computed from node feature vectors, the Louvain algorithm optimizes modularity iteratively, effectively partitioning graphs into cohesive communities. This iterative process facilitates the identification of significant network structures within complex networks, providing valuable insights into their organizational dynamics. To comprehensively evaluate LouvainSplit's performance, we integrated diverse graph partitioning algorithms, including PyMetis, Genetic Algorithm (GA), DBSCAN, KMeans, OPTICS, and spectral clustering to evaluate the effectiveness of LouvainSplit and using popular evaluation metrics across diverse benchmarks spanning various domains and graphs. The results demonstrate LouvainSplit's superiority in terms of recall score, modularity, and scalability compared to existing methods. Moreover, LouvainSplit maintains competitive average runtime values, ensuring efficient processing even with large-scale datasets.

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Section: -Introductionmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

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“…Recently, various approaches have evolved to discover communities in multiplex networks [7]. Earlier multiplex network community detection approaches interpreted the multiplex network as a monoplex network by aggregating the different layers into a single-layer, then discovering the community structure by applying traditional simple graph community detection techniques [8]. However, these techniques are not appropriate to reveal the true community structure, as they do not consider the proprieties carried in each layer.…”

Section: Introductionmentioning

confidence: 99%

Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

et al. 2023

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Complex networks arise in various fields, such as biology, sociology and communication, to model interactions among entities. Entities in many real-world systems exhibit different types of interactions, which requires modeling these type of systems properly. Multiplex networks are used to model these systems, as they can reflect the nodes’ pair-wise interactions as multiple distinct types of links across layers. Community detection is a widely studied application in network analysis as it provides insights into the structure and organization of the network. Even though multiple algorithms have been developed in the community detection field, many of them have a limited performance in the presence of noise. In this article, we develop a novel algorithm that combines tensor low-rank representation, spectral clustering and distance regularization to improve the accuracy in discovering communities in multiplex networks. The low-rank representation leads to reducing the noise and errors existing in the network and the optimization of an accurate consensus set of eigenvectors that reveals the communities in the network. Moreover, the proposed approach balances the agreement between the eigenvectors of each layer, i.e., individual subspaces, and the consensus set of eigenvectors, i.e., common subspaces, by minimizing the projection distance between them. The common and individual subspaces are computed efficiently through Tucker decomposition and modified spectral clustering, respectively. Finally, multiple experiments are conducted on real and simulated networks to evaluate the proposed approach and compare it to state-of-the-art algorithms. The proposed approach shows its robustness and efficiency in discovering the communities in multiplex networks.

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“…Essas duas técnicas têm sido aplicadas em diferentes domínios. O k-means tem sido utilizado em diversos campos, como detecc ¸ão dinâmica de comunidades em redes sociais online [34], classificac ¸ão de habilidades de estudantes [36], soluc ¸ões de recuperac ¸ão para nuvens híbridas [38], gerenciamento de conhecimento por meio do agrupamento de documentos [39] e detecc ¸ão robusta de comunidades em grafos [40]. Essas aplicac ¸ões destacam a versatilidade do algoritmo k-means na identificac ¸ão de clusters e comunidades em vários conjuntos de dados.…”

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Comparação entre a detecção de comunidades e o algoritmo K-Means na descoberta do espectro do Transtorno do Déficit de Atenção e Hiperatividade (TDAH)

Cassule,

Rodrigues Carreiro,

Silva

2023

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Transtorno do Déficit de Atenção/Hiperatividade (TDAH) é um transtorno do neurodesenvolvimento que afeta a atenção e o controle comportamental. Os sintomas são frequentemente tratados de forma individualizada, mas a heterogeneidade do transtorno sugere a necessidade de abordagens mais personalizadas. Este estudo utilizou uma rede complexa de pacientes com TDAH para investigar a possibilidade de diferentes perfis de prejuízo atencional. A análise da rede revelou a presença de dois grupos distintos: um grupo moderado e um grupo severo. O grupo moderado apresentou tempos de reação mais rápidos e maiores valores de centralidade do que o grupo severo. Essas descobertas sugerem que os dois grupos podem apresentar diferentes características atencionais, o que pode ter implicações para o diagnóstico e tratamento do TDAH. O estudo utilizou o algoritmo de clusterização KMeans para identificar os dois grupos. Os resultados do teste de hipótese forneceram suporte à hipótese alternativa, indicando que a divisão em dois grupos distintos com base na variabilidade atencional é estatisticamente significativa. Este estudo fornece evidências de que os pacientes com TDAH podem apresentar diferentes características atencionais. Essas descobertas podem levar ao desenvolvimento de abordagens de diagnóstico e tratamento mais personalizado para o TDAH.

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Robust Community Detection in Graphs

Cited by 8 publications

References 40 publications

Efficient Graph Partitioning using LouvainSplit Algorithm

Efficient Graph Partitioning using LouvainSplit Algorithm

Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

Comparação entre a detecção de comunidades e o algoritmo K-Means na descoberta do espectro do Transtorno do Déficit de Atenção e Hiperatividade (TDAH)

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