Abstract
In complex networks, especially social networks, networks could be divided into disjoint partitions. However, nodes could be partitioned such that the number of internal edges (the edges between the vertices within the same partition) to the number of outer edges (edges between two vertices of different partitions) is high. Generally, these partitions are called communities. Detecting community structure helps data scientists to extract meaningful information from networks and analyze them. In the last decades, various algorithms have been proposed to detect communities in graphs, and each one has examined this issue from a different perspective. Yet, most of these algorithms have a significant time complexity and costly calculations that make them unsuitable to detect communities in large graphs with millions of edges and nodes. Label propagation algorithm (LPA), a fast random-based algorithm, can easily detect communities in big graphs, but its accuracy compared to other algorithms is low. In this paper, we have improved LPA accuracy, and to achieve this goal, we propagate label based on the Local Edge Betweenness score. The proposed algorithm, named Weighted Label Propagation based on Local Edge Betweenness, is able to identify distinct communities in both the real-world and artificial networks. Also, the proposed algorithm could detect communities in weighted graphs. Empirical experiments show that the accuracy and speed of the proposed algorithm are acceptable; additionally, the proposed algorithm is scalable.
Similar content being viewed by others
References
Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E Statist Nonlinear Soft Matter Phys 72(2):1–4
Newman M (2010) Networks: an introduction. Oxford University Press, Oxford
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci US A 99(12):7821–6
Condon A, Karp RM (1999) Algorithms for graph partitioning on the planted partition model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1671:221–232
Shrivastavajmlr A, Li P (2014) In defense of MinHash over SimHash. Jmlr W&Cp 33(33):886–894
Gopalan PK, Blei DM (2013) Efficient discovery of overlapping communities in massive networks. Proc Natl Acad Sci U S A 110(36):14534–14539
Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E Stat Nonlinear Soft Matter Phys 78(4):1–5
Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci U S A 101(9):2658–2663
Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E Stat Nonlinear Soft Matter Phys 76(3):1–11
Gregory S (2008) Local betweenness for finding communities in networks. University of Bristol, Tech. Rep
Barber MJ, Clark JW (2009) Detecting network communities by propagating labels under constraints. Phys Rev E Stat Nonlinear Soft Matter Phys 80(2):1–11
Liu X, Murata T (2010) Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Phys A Stat Mech Appl 389(7):1493–1500
Xu-bin KANG, Cai-yan JIA (2013) An improved fast community detection algorithm based on label propagation. J Hefei Univ Technol (Natural Science) 1:11
Xing Y, Meng F, Yong ZM, Zhu MS, Sun G (2014) A node influence based label propagation algorithm for community detection in networks. The Scientific World Journal 2014:
Francisquini R, Rosset V, Nascimento MCV (2017) GA-LP: a genetic algorithm based on label propagation to detect communities in directed networks. Expert Syst Appl 74:127–138
Zhang X-K, 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
Zhuoxiang Z, Yitong W, Jiatang T, Zexu Z (2011) A novel algorithm for community discovery in social networkss based on label propagation. J Comput Res Dev 3:8–15
Lou H, Li S, Zhao Y (2013) Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Phys A Stat Mech Appl 392:3095–3105
Chen N, Liu Y, Chen H, Cheng J (2017) Detecting communities in social networks using label propagation with information entropy. Phys A Stat Mech Appl 471:788–798
Zhang X-K, Fei S, Song C, Tian X, Ao Y-Y (2015) Label propagation algorithm based on local cycles for community detection. Int J Mod Phys B 29(05):1550029
Xie J, Szymanski BK, Liu X (2011) SLPA: uncovering overlapping communities in social networks via a speaker–listener interaction dynamic process. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp 344–349. IEEE
Gregory S (2010) Finding overlapping communities in networks by label propagation. New J Phys 12(10):103018
Zhang X-K, Tian X, Li Y-N, Song C (2014) Label propagation algorithm based on edge clustering coefficient for community detection in complex networks. Int J Mod Phys B 28(30):1450216
Wang M, Wang C, Yu JX, Zhang J (2015) Community Detection in Social Networks : An In-depth Benchmarking Study with a Procedure-Oriented Framework. Proc VLDB Endow 8(10):998–1009
Pei T, Zhang H, Li Z, Choi Y (2014) Survey of community structure segmentation in complex networks. J Softw 9(1):89–93
Harenberg S, Bello G, Gjeltema L, Ranshous S, Harlalka J, Seay R, Padmanabhan K, Samatova N (2014) Community detection in large-scale networks: a survey and empirical evaluation. Wiley Interdiscip Rev Comput Stat 6(6):426–439
Xie J, Kelley S, Szymanski BK (2013) Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput Surv 45(4):43:1–43:35
Plantié Michel, Crampes Michel (2013) Survey on social community detection. Social Media Retrieval, pp 65–85
Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452–473
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):26113
McDaid AF, Greene D, Hurley N (2011) Normalized mutual information to evaluate overlapping community finding algorithms. arXiv preprintarXiv:1110.2515
Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations: can geographic isolation explain this unique trait? Behavi Ecol Sociobiol 54(4):396–405
Newman MEJ, Watts DJ, Strogatz SH (2002) Random graph models of social networks. Proc Natl Acad Sci 99(suppl 1):2566–2572
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shahrivari Joghan, H., Bagheri, A. & Azad, M. Weighted label propagation based on Local Edge Betweenness. J Supercomput 75, 8094–8114 (2019). https://doi.org/10.1007/s11227-019-02978-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-02978-4