Local core expanding-based label diffusion and local deep embedding for fast community detection algorithm in social networks

Basit Öğe Kaydı
dc.authorscopusidAsgarali Bouyer / 35177297800
dc.authorscopusidBahman Arasteh / 39861139000
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidAsgarali Bouyer / JOZ-6483-2023
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorShahgholi, Pouya
dc.contributor.authorArasteh, Bahman
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2025-04-18T10:48:20Z
dc.date.available2025-04-18T10:48:20Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractCommunity detection is a key task in social network analysis, as it reveals the underlying structure and function of the network. Various global and local techniques exist for uncovering community structures in social networks wherein diffusion-based algorithms are proposed as novel methods for local community detection, particularly suited for large-scale networks. The efficacy of diffusion processes and initial detection is paramount in the successful identification of community structures within social networks. This effectiveness hinges significantly on the meticulous selection of the label diffuser core, which serves as the foundation for propagating labels through the network, and the precise labeling of boundary nodes. Addressing the constraints of current community detection algorithms, notably their time complexity and efficiency, this paper proposes a novel local community detection algorithm that combines core expansion with label diffusion, and deep embedding techniques. In the proposed method, a new centrality measure is introduced for appropriate core selection to facilitate precise label diffusion in the initial phase. Subsequently, a deep embedding technique is employed for updating labels of boundary and core nodes using the GraphSage embedding method. Finally, a rapid merging step is executed to amalgamate initially proximate communities into finalized community structures in large-scale social networks. We evaluate our algorithm on 14 real-world and 4 synthetic networks and show that it outperforms existing methods in terms of NMI, F-measure, ARI, and modularity. According to numerical results, the proposed method shows approximately 1.04 %, 1.03 %, and 1.12 % improvement in F-measure, NMI, and ARI measures respectively, compared to the second-best method, LBLD, in the networks with ground-truth. In addition, our method is able to accurately identify communities in large-scale networks such as Orkut, YouTube, and LiveJournal, where it ranks among the top-performing methods. Our approach exhibits the best performance in terms of ARI compared to other algorithms under comparison.
dc.identifier.citationBouyer, A., Shahgholi, P., Arasteh, B., & Tirkolaee, E. B. (2024). Local core expanding-based label diffusion and local deep embedding for fast community detection algorithm in social networks. Computers and Electrical Engineering, 119, 109502.
dc.identifier.doi10.1016/j.compeleceng.2024.109502
dc.identifier.endpage19
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85199387862
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.compeleceng.2024.109502
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7192
dc.identifier.volume119
dc.identifier.wosWOS:001281388800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBouyer, Asgarali
dc.institutionauthorArasteh, Bahman
dc.institutionauthorTirkolaee, Erfan Babaee
dc.institutionauthoridAsgarali Bouyer / 0000-0002-4808-2856
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.institutionauthoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.language.isoen
dc.publisherPergamon-elsevier science ltd
dc.relation.ispartofComputers and electrical engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectSocial Networks
dc.subjectCommunity Detection
dc.subjectCore Expansion
dc.subjectLabel Diffusion
dc.subjectDeep Embedding
dc.subjectGraphSage Embedding
dc.titleLocal core expanding-based label diffusion and local deep embedding for fast community detection algorithm in social networks
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bilgisayar Mühendisliği Bölümü Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu