A community detection model using node embedding approach and graph convolutional network with clustering technique

Bhattacharya, Riju; Nagwani, Naresh Kumar; Tripathi, Sarsij

doi:10.1016/j.dajour.2023.100362

Cited by 2 publications

(1 citation statement)

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“…Modularity measures how much the network differs from a random network with the same degree distribution in terms of the number of edges within communities. Modularity maximization aims to find a grouping of nodes into communities that have the highest modularity score (Bhattacharya et al, 2023) (Contreras-Aso et al, 2023). This means finding clusters of nodes that have more connections among themselves than with other nodes.…”

Section: Modularitymentioning

confidence: 99%

RETRACTED: A Novel Weight-based Fish School Search Approach for Hierarchical Network Clustering

Ibrahim,

BOUDREF,

BADIS

2023

Preprint

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Networks consist of interconnected nodes and edges that depict entities and their relationships. In social network clustering, nodes are grouped into clusters based on their connectivity, to identify communities. However, community detection methods have not yet leveraged the Weight-based Fish School Search algorithm, which is one of the promising approaches to finding community structure. In this paper, we aim to apply a specific class of FSS-Based algorithm, which is weighted FSS, to network clustering. We have developed a unique hierarchical network clustering method that leverages the Weight-based Fish School Search algorithm (WFSSC). This methodology focuses on maximizing weights to enhance the modularity function, leading to the identification of community structures in unipartite, undirected, and weighted networks. The process involves iterative network splitting and the construction of a dendrogram, with the optimal community structure determined by selecting the cut that maximizes modularity. Our method employs the modularity function for an objective assessment of the community structure, aiding in optimal network division. We evaluated our methodology on known and unknown network structures, including a network generated using the LFR model to assess its adaptability to different community structures. The performance was measured using metrics such as NMI, ARI, and FMI. The results demonstrated that our methodology exhibits robust performance in identifying community structures, highlighting its effectiveness in capturing cohesive communities and accurately pinpointing actual community structures.

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Section: Modularitymentioning

confidence: 99%