A novel attributed community detection by integration of feature weighting and node centrality

被引:6
作者
Rostami, Mehrdad [1 ]
Oussalah, Mourad [1 ]
机构
[1] Univ Oulu, Fac Informat Technol, Ctr Machine Vis & Signal Proc, Oulu, Finland
基金
芬兰科学院;
关键词
Social network analysis; Community detection; Attributed social network; Attributed graph clustering; Feature weighting; Node centrality; GENETIC ALGORITHM; OPTIMIZATION; NETWORKS;
D O I
10.1016/j.osnem.2022.100219
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Community detection is one of the primary problems in social network analysis and this problem has more challenges in attributed social networks. The purpose of community detection in attributed social networks is to discover communities with not only homogeneous node properties but also adherent structures. Although community detection has been extensively studied, attributed community detection of large social networks with a large number of attributes remains a vital challenge. To address this challenge, in this paper a novel attributed community detection method is developed by integration of feature weighting with node centrality techniques. The developed method includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-based Attributed Community Detection. The aim of the first phase is to calculate the weight between two linked nodes using structural and attribute similarities, while, in the second phase, an improved label propagation algorithm-based community detection method in the attributed social network is proposed. The purpose of the second phase is to detect different communities by employing the calculated weight matrix and node popularity. After implementing the proposed method, its performance is compared with several other state of the art methods using some benchmarked real-world datasets. The results indicate that the developed method outperforms several other state-of-the-art methods and ascertain the effectiveness of the developed method for attributed community detection.
引用
收藏
页数:14
相关论文
共 41 条
[1]   A new similarity measure for link prediction based on local structures in social networks [J].
Aghabozorgi, Farshad ;
Khayyambashi, Mohammad Reza .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 501 :12-23
[2]  
Akbas E., 2017, P 2017 IEEEACM INT C, P305
[3]   A two-layer feature selection method using Genetic Algorithm and Elastic Net [J].
Amini, Fatemeh ;
Hu, Guiping .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 166
[4]  
[Anonymous], 2005, Advances in Neural Information Processing Systems
[5]   Unsupervised feature selection and NMF de-noising for robust Speech Emotion Recognition [J].
Bandela, Surekha Reddy ;
Kumar, T. Kishore .
APPLIED ACOUSTICS, 2021, 172 (172)
[6]   A Link-Based Similarity for Improving Community Detection Based on Label Propagation Algorithm [J].
Berahmand, Kamal ;
Bouyer, Asgarali .
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2019, 32 (03) :737-758
[7]   Community detection in node-attributed social networks: A survey [J].
Chunaev, Petr .
COMPUTER SCIENCE REVIEW, 2020, 37
[8]   Geometrical inspired pre-weighting enhances Markov clustering community detection in complex networks [J].
Duran, Claudio ;
Muscoloni, Alessandro ;
Cannistraci, Carlo Vittorio .
APPLIED NETWORK SCIENCE, 2021, 6 (01)
[9]   Semidefinite Programming for Community Detection With Side Information [J].
Esmaeili, Mohammad ;
Saad, Hussein Metwaly ;
Nosratinia, Aria .
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2021, 8 (02) :1957-1973
[10]   A comparison of alternative tests of significance for the problem of m rankings [J].
Friedman, M .
ANNALS OF MATHEMATICAL STATISTICS, 1940, 11 :86-92