Evidence map›Paper›PMID 40857301›Full record

ArticlePloS one2025

Overlapping community detection based on bridging structural features and fuzzy C-means.

Ailian Wang, Mingwu Li, Xuyang Gao, Bolin Li, Zhiqiang Su

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ailian WangCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.
Mingwu LiCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.ORCID https://orcid.org/0009-0000-9313-0805
Xuyang GaoCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.
Bolin LiCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.
Zhiqiang SuCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, research on community structure for complex networks has received increasing greater attention, and the overlapping community structure is more closely related to the actual social structure than the non-overlapping community structure, so it is necessary to identify and detect the overlapping communities of social networks. In this paper, we propose an overlapping community optimization method (OSFCM) based on network structure characteristics and fuzzy C-means clustering. We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. Experimental results show that the algorithm exhibits higher delineation accuracy and better algorithmic performance on seven real network datasets and four synthetic networks.

Indexed as

Fuzzy LogicSocial NetworkingAlgorithmsCluster AnalysisHumans

Identifiers

PMID40857301
PMCPMC12380298

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.