دورية أكاديمية

Node Local Similarity Based Two-stage Density Peaks Algorithm for Overlapping Community Detection

التفاصيل البيبلوغرافية
العنوان: Node Local Similarity Based Two-stage Density Peaks Algorithm for Overlapping Community Detection
المؤلفون: DUAN Xiao-hu, CAO Fu-yuan
المصدر: Jisuanji kexue, Vol 49, Iss 12, Pp 170-177 (2022)
بيانات النشر: Editorial office of Computer Science, 2022.
سنة النشر: 2022
المجموعة: LCC:Computer software
LCC:Technology (General)
مصطلحات موضوعية: overlapping community detection, density peaks, node similarity, k-nearest neighbors, degree of membership, Computer software, QA76.75-76.765, Technology (General), T1-995
الوصف: In order to detect overlapping community structures in complex networks,the idea of density peaks clustering algorithm is introduced.However,applying the density peaks clustering algorithm to community detection still has problems such as how to measure the distance between nodes and how to generate overlapping partition results.Therefore,a node local similarity based two-stage density peaks algorithm for overlapping community detection is proposed (LSDPC).By combining hub promoted index and connection contribution degree,a new node local similarity index is defined,and the node distance is measured with the node local similarity.Then the local density and minimum distance of nodes are used to calculate their center values and Chebyshev inequality is used to select communities’ center nodes.The overlapping communities are obtained through initial assignment and overlapping assignment.Experimental results on real network datasets and synthetic network datasets show that the proposed algorithm can effectively detect overlapping community structure,and the results are better than that of other algorithms.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: Chinese
تدمد: 1002-137X
Relation: https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-12-170.pdf; https://doaj.org/toc/1002-137X
DOI: 10.11896/jsjkx.211000025
URL الوصول: https://doaj.org/article/afb1b4473d6d44c7bcc1c96ef67571a8
رقم الأكسشن: edsdoj.fb1b4473d6d44c7bcc1c96ef67571a8
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:1002137X
DOI:10.11896/jsjkx.211000025