A novel algorithm infomap-SA of detecting communities in complex networks

被引:7
作者
Hu, Fang [1 ,2 ]
Liu, Yuhua [1 ]
机构
[1] Schoolof Computer Science, Central China Normal University, Wuhan
[2] College of Information Engineering, Hubei University of Chinese Medicine, Wuhan
来源
Journal of Communications | 2015年 / 10卷 / 07期
关键词
Community detection; Density; Infomap-simulated annealing algorithm; Modularity; Simulation test;
D O I
10.12720/jcm.10.7.503-511
中图分类号
学科分类号
摘要
Community detection is one of the most important issues in complex networks. In this paper, integrating Infomap and Simulated Annealing (SA) algorithm, and based on the thought of optimizationof the modularity function, the authors are proposing a novel algorithm Infomap-SA for detecting community. In order to verify the accuracy and efficiency of this algorithm, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks by LFR-benchmark. The experimental results show that this algorithm can identify the communities accurately and efficiently, and has higher values of modularity and density and lower computable complexity than Infomap algorithm. Furthermore, the Infomap-SA is more suitable for community detection of large-scale network. © 2015 Journal of Communications.
引用
收藏
页码:503 / 511
页数:8
相关论文
共 50 条
  • [21] Detecting Communities in Complex Networks Using an Adaptive Genetic Algorithm and Node Similarity-Based Encoding
    Hesamipour, Sajjad
    Balafar, Mohammad Ali
    Mousazadeh, Saeed
    COMPLEXITY, 2023, 2023
  • [22] Detecting composite communities in multiplex networks: A multilevel memetic algorithm
    Ma, Lijia
    Gong, Maoguo
    Yan, Jianan
    Liu, Wenfeng
    Wang, Shanfeng
    SWARM AND EVOLUTIONARY COMPUTATION, 2018, 39 : 177 - 191
  • [23] A hybrid artificial immune network for detecting communities in complex networks
    Karimi-Majd, Amir-Mohsen
    Fathian, Mohammad
    Amiri, Babak
    COMPUTING, 2015, 97 (05) : 483 - 507
  • [24] A hybrid artificial immune network for detecting communities in complex networks
    Amir-Mohsen Karimi-Majd
    Mohammad Fathian
    Babak Amiri
    Computing, 2015, 97 : 483 - 507
  • [25] Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm
    Selvaraj, Devi
    Murugasamy, Rajalakshmi
    ACTA INFORMATICA PRAGENSIA, 2022, 11 (03) : 380 - 395
  • [26] A Multiobjective Genetic Algorithm to Find Communities in Complex Networks
    Pizzuti, Clara
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2012, 16 (03) : 418 - 430
  • [27] A Rough Connectedness Algorithm for Mining Communities in Complex Networks
    Gupta, Samrat
    Kumar, Pradeep
    Bhasker, Bharat
    BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY, DAWAK 2016, 2016, 9829 : 34 - 48
  • [28] CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks
    Said, Anwar
    Abbasi, Rabeeh Ayaz
    Maqbool, Onaiza
    Daud, Ali
    Aljohani, Naif Radi
    APPLIED SOFT COMPUTING, 2018, 63 : 59 - 70
  • [29] A Novel NMF Algorithm for Detecting Clusters in Directed Networks
    Usuzaka, Yoshito
    Takahashi, Norikazu
    2019 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC), 2019, : 148 - 152
  • [30] Detecting Overlapping Protein Communities in Disease Networks
    Mahmoud, Hassan
    Masulli, Francesco
    Rovetta, Stefano
    Russo, Giuseppe
    COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, CIBB 2014, 2015, 8623 : 109 - 120