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An improved Girvan–Newman community detection algorithm using trust-based centrality

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Abstract

Accumulative structure or cluster-like shape is one of the important features of social networks. These structures and clusters are communities in a complex network and are fully detectable. Common group behaviors of different communities can be categorized using community detection methods. Categorize behavior allows the study of each part of the network to be done centrally. This paper uses trust-based centrality to detect the communities that make up the network. Centrality determines the relative importance of a node in the graph of social networks. Redefining the trust-based centrality makes it possible to change the position in the analysis of centrality and separates the local central nodes and global central nodes. Then, a trust-based algorithm is proposed to express the strength of trust penetration conceptually between nodes to extract communities in networks. This method has led to the achievement of a flexible and effective community detection method. The proposed algorithm is applied to four benchmark networks. The experiments consist of two independent parts. The first part is to use the proposed algorithm to detect clusters and communities. After that, the algorithm is compared with a Girvan–Newman inspired method. The second part is the implementation of the proposed algorithm with a large number of iterations with the aim of modularity maximization and comparing it with other community detection algorithms. Although, the modularity criterion has been used to validate and compare the solution quality in both independent parts of the experiments. The results show about 1.4–5.2% improvement in community detection.

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References

  • Ahn YY, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466(7307):761–764

    Article  Google Scholar 

  • Alghamdi E, Greene D (2019) Active semi-supervised overlapping community finding with pairwise constraints. Appl Netw Sci 4(1):1–27

    Article  Google Scholar 

  • Barabási AL (2013) Network science. Philos Trans R Soc A 371(1987):20120375

    Article  Google Scholar 

  • Carnivali GS, Vieira AB, Ziviani A, Esquef PA (2020) CoVeC: coarse-grained vertex clustering for efficient community detection in sparse complex networks. Inf Sci 522:180–192

    Article  MathSciNet  Google Scholar 

  • Chen X, Xia C, Wang J (2018) A novel trust-based community detection algorithm used in social networks. Chaos Solitons Fract 108:57–65

    Article  MathSciNet  Google Scholar 

  • Danowski JA (2012) Social media network size and semantic networks for collaboration in design. Int J Organ Des Eng 2(4):343–361

    Google Scholar 

  • Deng ZH, Qiao HH, Song Q, Gao L (2019) A complex network community detection algorithm based on label propagation and fuzzy C-means. Physica A 519:217–226

    Article  Google Scholar 

  • Du N, Wang B, Wu B, Wang Y (2008) Overlapping community detection in bipartite networks. In 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, IEEE, pp 176–179

  • Freeman LC (2011) The development of social network analysis—with an emphasis on recent events. SAGE Handb Soc Netw Anal 21(3):26–39

    Google Scholar 

  • Gilbert F, Simonetto P, Zaidi F, Jourdan F, Bourqui R (2011) Communities and hierarchical structures in dynamic social networks: analysis and visualization. Soc Netw Anal Min 1(2):83–95

    Article  Google Scholar 

  • Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  • Grabner-Kräuter S, Bitter S (2015) Trust in online social networks: a multifaceted perspective. Forum Soc Econ 44(1):48–68

    Article  Google Scholar 

  • Guo K, He L, Chen Y, Guo W, Zheng J (2020) A local community detection algorithm based on internal force between nodes. Appl Intell 50(2):328–340

    Article  Google Scholar 

  • Harifi S, Mohammadzadeh J, Khalilian M, Ebrahimnejad S (2020) Giza Pyramids Construction: an ancient-inspired metaheuristic algorithm for optimization. Evol Intel. https://doi.org/10.1007/s12065-020-00451-3

    Article  Google Scholar 

  • Harifi S, Mohammadzadeh J, Khalilian M, Ebrahimnejad S (2021) Hybrid-EPC: an Emperor Penguins Colony algorithm with crossover and mutation operators and its application in community detection. Prog Artif Intell 10(2):181–193

    Article  Google Scholar 

  • Hu F, Liu J, Li L, Liang J (2020) Community detection in complex networks using Node2vec with spectral clustering. Physica A 545:123633

    Article  Google Scholar 

  • Ji P, Zhang S, Zhou Z (2020) A decomposition-based ant colony optimization algorithm for the multi-objective community detection. J Ambient Intell Humaniz Comput 11(1):173–188

    Article  Google Scholar 

  • Jiang H, Liu Z, Liu C, Su Y, Zhang X (2020) Community detection in complex networks with an ambiguous structure using central node based link prediction. Knowl-Based Syst 195:105626

    Article  Google Scholar 

  • Jin JH, Park SC, Pyon CU (2011) Finding research trend of convergence technology based on Korean R&D network. Expert Syst Appl 38(12):15159–15171

    Article  Google Scholar 

  • Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291–307

    Article  MATH  Google Scholar 

  • Kırer H, Çırpıcı YA (2016) A survey of agent-based approach of complex networks. Ekonomik Yaklasim 27(98):1–28

    Article  Google Scholar 

  • Kumar S, Carley KM (2018) Towards group-activities based community detection. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers, ACM, pp 1178–1183

  • Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110

    Article  Google Scholar 

  • Mahajan SP, Raipurkar AR (2018) Network based community detection by using bisecting hierarchical clustering. HELIX 8(5):4077–4081

    Article  Google Scholar 

  • Miyauchi A, Kawase Y (2016) Z-score-based modularity for community detection in networks. PLoS ONE 11(1):e0147805

    Article  Google Scholar 

  • Moosavi SA, Jalali M, Misaghian N, Shamshirband S, Anisi MH (2017) Community detection in social networks using user frequent pattern mining. Knowl Inf Syst 51(1):159–186

    Article  Google Scholar 

  • Muller E, Peres R (2019) The effect of social networks structure on innovation performance: a review and directions for research. Int J Res Mark 36(1):3–19

    Article  Google Scholar 

  • Nerurkar P, Chandane M, Bhirud S (2019) A comparative analysis of community detection algorithms on social networks. In: Verma N, Ghosh A (eds) Computational intelligence: theories, applications and future directions—Volume I. Advances in intelligent systems and computing. Springer, pp 287–298

    Google Scholar 

  • Nicosia V, Mangioni G, Carchiolo V, Malgeri M (2009) Extending the definition of modularity to directed graphs with overlapping communities. J Stat Mech Theory Exp 2009(03):P03024

    Article  Google Scholar 

  • Nikolaev AG, Razib R, Kucheriya A (2015) On efficient use of entropy centrality for social network analysis and community detection. Soc Netw 40:154–162

    Article  Google Scholar 

  • Rosvall M, Delvenne JC, Schaub MT, Lambiotte R (2019) Different approaches to community detection. In: Batagelj V, Ferligoj A (eds) Doreian P. Advances in network clustering and blockmodeling, Wiley, pp 105–119

    Google Scholar 

  • Wang GA, Jiao J, Abrahams AS, Fan W, Zhang Z (2013) ExpertRank: a topic-aware expert finding algorithm for online knowledge communities. Decis Support Syst 54(3):1442–1451

    Article  Google Scholar 

  • Xiao J, Ren HF, Xu XK (2020) Constructing real-life benchmarks for community detection by rewiring edges. Complexity 2020:1–16

    Google Scholar 

  • Yang Z, Algesheimer R, Tessone CJ (2016) A comparative analysis of community detection algorithms on artificial networks. Sci Rep 6:30750

    Article  Google Scholar 

  • Zhang Z, Wang Z (2015) Mining overlapping and hierarchical communities in complex networks. Phys A Stat Mech Appl 421:25–33

    Article  Google Scholar 

  • Zhang L, Ye Q, Shao Y, Li C, Gao H (2014) An efficient hierarchy algorithm for community detection in complex networks. Math Probl Eng 2014:1–12

    Article  Google Scholar 

  • Zhang XK, Ren J, Song C, Jia J, Zhang Q (2017) Label propagation algorithm for community detection based on node importance and label influence. Phys Lett A 381(33):2691–2698

    Article  MathSciNet  MATH  Google Scholar 

  • Zhao P, Zhang CQ (2011) A new clustering method and its application in social networks. Pattern Recogn Lett 32(15):2109–2118

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments and suggestions that improved the presentation of this paper.

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Authors and Affiliations

  1. Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    Mahsa Zahiri, Javad Mohammadzadeh & Sasan Harifi

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  1. Mahsa Zahiri
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  2. Javad Mohammadzadeh
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  3. Sasan Harifi
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Correspondence to Javad Mohammadzadeh.

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Zahiri, M., Mohammadzadeh, J. & Harifi, S. An improved Girvan–Newman community detection algorithm using trust-based centrality. J Ambient Intell Human Comput 14, 3755–3766 (2023). https://doi.org/10.1007/s12652-021-03508-y

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