Mapping Connections between Neighborhoods in Response to Community-Based Social Needs

Bevilacqua, Carmelina; Sohrabi, Poya; Hamdy, Nourhan; Mangiulli, Federica

doi:10.3390/su15064898

Cited by 7 publications

(1 citation statement)

References 40 publications

(53 reference statements)

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“…The clustering process in social networks typically revolves around identifying communities. A community refers to a group of nodes within a network that exhibit dense interconnections but have relatively fewer connections with nodes outside the community (Bevilacqua et al, 2023). The objective of community detection is to identify these communities or clusters of nodes within a network, where the nodes within a community are more densely connected than nodes in other communities (Mester et al, 2021) (Rashid & Bhat, 2023).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%