Skip to main content
SpringerLink
Log in

Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern mining

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Community detection is a significant research area in social networks. Most methods use network topology, but combining it with user interactions improves accuracy. This paper proposes a robust method to identify communities based on the improved user interaction degree, the weighted quasi-local structural similarity measure, and the frequent pattern mining on user interactions. In the community creation phase, influential users are identified based on eigenvector centrality and users who interact with them the most are extracted based on frequent pattern mining. In the community expansion phase, we introduce a measure to calculate the degree of user interactions based on the local clustering coefficient improved by interactions between common neighbors. We present two strategies to expand the community. The first strategy, a direct connection, exists between a user outside and a user inside the community. Their similarity is calculated based on the combined measure of improved user interaction degree and user degrees. The second strategy is if two users do not have a direct connection, we consider their communication paths. Therefore, we present a similarity measure combining a quasi-local path-based measure and an improved user interaction degree. Analysis of Higgs Twitter and Flickr datasets using internal density, Normalized Mutual Information, and Adjusted Rand Index shows that this paper's method outperforms the other five community detection methods. Furthermore, our method has more robustness than other relevant methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The data that support the findings of this study are openly available in Stanford Network Analysis Platform (SNAP) at http://snap.stanford.edu/data/higgs-twitter.html, reference number [53] and in ArnetMine at https://www.aminer.cn/data-sna#Flickr, reference number [55].

References

  1. Li X, Xu G, Tang M (2018) Community detection for multi-layer social network based on local random walk. J Visual Commun Image Represent 57:91–98. https://doi.org/10.1016/j.jvcir.2018.10.003

    Article  Google Scholar 

  2. Dabaghi-Zarandi F, KamaliPour P (2022) Community detection in complex network based on an improved random algorithm using local and global network information. J Network Comput Appl 206:103492. https://doi.org/10.1016/j.jnca.2022.103492

    Article  Google Scholar 

  3. Das BC, Anwar MM, Bhuiyan MA-A, Sarker IH, Alyami SA, Moni MA (2021) Attribute driven temporal active online community search. IEEE Access 9:93976–93989. https://doi.org/10.1109/ACCESS.2021.3093368

    Article  Google Scholar 

  4. Moscato V, Sperlì G (2021) A survey about community detection over On-line Social and Heterogeneous Information Networks. Knowledge-Based Syst 224:107112. https://doi.org/10.1016/j.knosys.2021.107112

    Article  Google Scholar 

  5. Luo L, Liu K, Guo B, Ma J (2020) User interaction-oriented community detection based on cascading analysis. Inf Sci 510:70–88. https://doi.org/10.1016/j.ins.2019.09.022

    Article  Google Scholar 

  6. Wilson C, Sala A, Puttaswamy KPN, Zhao BY (2012) Beyond Social Graphs. ACM Trans Web 6:1–31. https://doi.org/10.1145/2382616.2382620

    Article  Google Scholar 

  7. O’Riordan S, Feller J, Nagle T (2016) A categorisation framework for a feature-level analysis of social network sites. J Decis Syst 25:244–262. https://doi.org/10.1080/12460125.2016.1187548

    Article  Google Scholar 

  8. Moosavi SA, Jalali M, Misaghian N, Shamshirband S, Anisi MH (2016) Community detection in social networks using user frequent pattern mining. Knowl Inf Syst 51:159–186. https://doi.org/10.1007/s10115-016-0970-8

    Article  Google Scholar 

  9. Dev H, Ali ME, Hashem T (2014) User interaction based community detection in online social networks. In: Database Systems for Advanced Applications: 19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014. Proceedings, Part II 19, 296-310, Springer. https://doi.org/10.1007/978-3-319-05813-9_20

  10. Vathi E, Siolas G, Stafylopatis A, Nguyen N-T, Núñez M, Trawiński B (2017) Mining and categorizing interesting topics in Twitter communities. J Intell Fuzzy Syst 32:1265–1275. https://doi.org/10.3233/jifs-169125

    Article  Google Scholar 

  11. Kumar S, Mallik A, Khetarpal A, Panda BS (2022) Influence maximization in social networks using graph embedding and graph neural network. Inf Sci 607:1617–1636. https://doi.org/10.1016/j.ins.2022.06.075

    Article  Google Scholar 

  12. Ai J, He T, Su Z, Shang L (2022) Identifying influential nodes in complex networks based on spreading probability. Chaos, Solitons Fractals 164:112627. https://doi.org/10.1016/j.chaos.2022.112627

    Article  Google Scholar 

  13. Laeuchli J, Ramírez-Cruz Y, Trujillo-Rasua R (2022) Analysis of centrality measures under differential privacy models. Appl Math Comput 412:126546. https://doi.org/10.1016/j.amc.2021.126546

    Article  MathSciNet  Google Scholar 

  14. Hansen D, Shneiderman B, Smith MA (2020) Analyzing social media networks with NodeXL: insights from a connected world (Second Edition), Morgan Kaufmann pp.Chapter 3. https://doi.org/10.1016/C2018-0-01348-1

  15. Samanta S, Dubey VK, Sarkar B (2021) Measure of influences in social networks. Appl Soft Comput 99:106858. https://doi.org/10.1016/j.asoc.2020.106858

    Article  Google Scholar 

  16. Zhong L-F, Shang M-S, Chen X-L, Cai S-M (2018) Identifying the influential nodes via eigen-centrality from the differences and similarities of structure. Phys A Stat Mech Appl 510:77–82. https://doi.org/10.1016/j.physa.2018.06.115

    Article  Google Scholar 

  17. Goyal A, Bonchi F, Lakshmanan LV (2008) Discovering leaders from community actions. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, 499–508, https://doi.org/10.1145/1458082.1458149

  18. Lu D, Li Q, Liao SS (2012) A graph-based action network framework to identify prestigious members through member’s prestige evolution. Decis Support Syst 53:44–54. https://doi.org/10.1016/j.dss.2011.12.003

    Article  Google Scholar 

  19. Bamakan SMH, Nurgaliev I, Qu Q (2019) Opinion leader detection: a methodological review. Expert Syst Appl 115:200–222. https://doi.org/10.1016/j.eswa.2018.07.069

    Article  Google Scholar 

  20. Kolahkaj M, Harounabadi A, Nikravanshalmani A, Chinipardaz R (2020) A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining. Electron Commer Res App 42:100978. https://doi.org/10.1016/j.elerap.2020.100978

    Article  Google Scholar 

  21. Noorian A, Harounabadi A, Ravanmehr R (2022) A novel Sequence-Aware personalized recommendation system based on multidimensional information. Expert Syst Appl 202:117079. https://doi.org/10.1016/j.eswa.2022.117079

    Article  Google Scholar 

  22. Martínez V, Berzal F, Cubero J-C (2016) A survey of link prediction in complex networks. ACM Comput Surv 49:1–33. https://doi.org/10.1145/3012704

    Article  Google Scholar 

  23. Srilatha P, Manjula R (2016) Similarity index based link prediction algorithms in social networks: a survey. J Telecommun Inf Technol 2:87–94

    Google Scholar 

  24. Tumiran SA, Sivakumar B (2021) Community structure concept for catchment classification: a modularity density-based edge betweenness (MDEB) method. Ecol Indic 124:107346. https://doi.org/10.1016/j.ecolind.2021.107346

    Article  Google Scholar 

  25. Fardet T, Levina A (2021) Weighted directed clustering: interpretations and requirements for heterogeneous, inferred, and measured networks. Phys Rev Res. https://doi.org/10.1103/PhysRevResearch.3.043124

    Article  Google Scholar 

  26. Paul A, Dutta A (2022) Community detection using Local Group Assimilation. Expert Syst Appl 206:117794. https://doi.org/10.1016/j.eswa.2022.117794

    Article  Google Scholar 

  27. Shang R, Zhang W, Li Z, Wang C, Jiao L (2023) Attribute community detection based on latent representation learning and graph regularized non-negative matrix factorization. Appl Soft Comput 133:109932. https://doi.org/10.1016/j.asoc.2022.109932

    Article  Google Scholar 

  28. Berahmand K, Bouyer A (2018) A link-based similarity for improving community detection based on label propagation algorithm. J Syst Sci Complexity 32:737–758. https://doi.org/10.1007/s11424-018-7270-1

    Article  Google Scholar 

  29. Arab M, Afsharchi M (2014) Community detection in social networks using hybrid merging of sub-communities. J Network Comput Appl 40:73–84. https://doi.org/10.1016/j.jnca.2013.08.008

    Article  Google Scholar 

  30. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008:P10008. https://doi.org/10.1088/1742-5468/2008/10/p10008

    Article  Google Scholar 

  31. Dugué N, Perez A (2022) Direction matters in complex networks: a theoretical and applied study for greedy modularity optimization. Phys A Stat Mech Appl 603:127798. https://doi.org/10.1016/j.physa.2022.127798

    Article  MathSciNet  Google Scholar 

  32. Yakoubi Z, Kanawati R (2014) LICOD: a Leader-driven algorithm for community detection in complex networks. Vietnam J Comput Sci 1:241–256. https://doi.org/10.1007/s40595-014-0025-6

    Article  Google Scholar 

  33. Ahajjam S, El Haddad M, Badir H (2018) A new scalable leader-community detection approach for community detection in social networks. Soc Netw 54:41–49. https://doi.org/10.1016/j.socnet.2017.11.004

    Article  Google Scholar 

  34. Belfin RV, Grace Mary Kanaga E, Piotr B (2018) Overlapping community detection using superior seed set selection in social networks. Comput Electr Eng 70:1074–1083. https://doi.org/10.1016/j.compeleceng.2018.03.012

    Article  Google Scholar 

  35. Li W, Huang C, Wang M, Chen X (2017) Stepping community detection algorithm based on label propagation and similarity. Phys A Stat Mech Appl 472:145–155. https://doi.org/10.1016/j.physa.2017.01.030

    Article  Google Scholar 

  36. Pan X, Xu G, Wang B, Zhang T (2019) A novel community detection algorithm based on local similarity of clustering coefficient in social networks. IEEE Access 7:121586–121598. https://doi.org/10.1109/access.2019.2937580

    Article  Google Scholar 

  37. Jaouadi M, Romdhane LB (2016) DIN: an efficient algorithm for detecting influential nodes in social graphs using network structure and attributes. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 1–8, IEEE. https://doi.org/10.1109/AICCSA.2016.7945698

  38. Wang Y, Jin D, He D, Musial K, Dang J (2022) Community detection in social networks considering social behaviors. IEEE Access 10:109969–109982. https://doi.org/10.1109/ACCESS.2022.3209704

    Article  Google Scholar 

  39. Gupta SK, Singh DP (2023) Seed community identification framework for community detection over social media. Arab J Sci Eng 48:1829–1843. https://doi.org/10.1007/s13369-022-07020-z

    Article  Google Scholar 

  40. Reihanian A, Feizi-Derakhshi M-R, Aghdasi HS (2023) An enhanced multi-objective biogeography-based optimization for overlapping community detection in social networks with node attributes. Inf Sci 622:903–929. https://doi.org/10.1016/j.ins.2022.11.125

    Article  Google Scholar 

  41. Ahmed C, ElKorany A (2015) Enhancing link prediction in Twitter using semantic user attributes. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 1155–1161, https://doi.org/10.1145/2808797.2810056

  42. Yang C, Liu L, Chen L, Niu B (2017) A novel friend recommendation service based on interaction information mining. In: 2017 International Conference on Service Systems and Service Management, 1–5, IEEE. https://doi.org/10.1109/ICSSSM.2017.7996287

  43. Lim KH, Datta A (2016) An interaction-based approach to detecting highly interactive Twitter communities using tweeting links. Web Intell 14:1–15. https://doi.org/10.3233/web-160328

    Article  Google Scholar 

  44. Helal NA, Ismail RM, Badr NL, Mostafa MGM (2017) Leader-based community detection algorithm for social networks. WIREs Data Min Knowl Discovery. https://doi.org/10.1002/widm.1213

    Article  Google Scholar 

  45. Ma X, He J, Wu T, Zhu N, Hua Y (2023) Interaction behavior enhanced community detection in online social networks. Comput Commun. https://doi.org/10.1016/j.comcom.2023.11.029

    Article  Google Scholar 

  46. Newman M (2018) Networks Second Edition. Oxford University Press. https://doi.org/10.1093/oso/9780198805090.003.0007

  47. Meng X, Han S, Wu L, Si S, Cai Z (2022) Analysis of epidemic vaccination strategies by node importance and evolutionary game on complex networks. Reliab Eng Syst Saf 219:108256. https://doi.org/10.1016/j.ress.2021.108256

    Article  Google Scholar 

  48. Bai Z-Z, Wu W-T, Muratova GV (2021) The power method and beyond. Appl Numer Math 164:29–42. https://doi.org/10.1016/j.apnum.2020.03.021

    Article  MathSciNet  Google Scholar 

  49. Xiao W, Hu J (2021) Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce. Microprocess Microsyst 82:103817. https://doi.org/10.1016/j.micpro.2020.103817

    Article  Google Scholar 

  50. Telikani A, Gandomi AH, Shahbahrami A (2020) A survey of evolutionary computation for association rule mining. Inf Sci 524:318–352. https://doi.org/10.1016/j.ins.2020.02.073

    Article  MathSciNet  Google Scholar 

  51. Yang R, Yang C, Peng X, Rezaeipanah A (2022) A novel similarity measure of link prediction in multi-layer social networks based on reliable paths. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.6829

    Article  Google Scholar 

  52. De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980. https://doi.org/10.1038/srep02980

    Article  Google Scholar 

  53. Platform SNA and (SNAP) higgs-twitter (Accessed February 18, 2023) (2015 ) http://snap.stanford.edu/data/higgs-twitter.html

  54. Tan C, Tang J, Sun J, Lin Q, Wang F (2010) Social action tracking via noise tolerant time-varying factor graphs. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1049–1058, https://doi.org/10.1145/1835804.1835936

  55. (ArnetMiner), A. Flickr-large Accessed February 18, 2023 (2006) https://www.aminer.cn/data-sna#Flickrlarge

  56. Singh D, Garg R (2022) NI-Louvain: a novel algorithm to detect overlapping communities with influence analysis. J King Saud Univ Comput Inf Sci 34:7765–7774. https://doi.org/10.1016/j.jksuci.2021.07.006

    Article  Google Scholar 

  57. 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, 344–349, IEEE. https://doi.org/10.1109/ICDMW.2011.154

  58. Meghanathan N (2015) Use of eigenvector centrality to detect graph isomorphism. arXiv preprint arXiv:1511.06620. https://doi.org/10.5121/csit.2015.51501

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

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

    Somaye Sayari, Ali Harounabadi & Touraj Banirostam

Authors
  1. Somaye Sayari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Harounabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Touraj Banirostam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S. Sayari, A. Harunabadi, and T. Banirostam contributed to presenting the initial idea, developing and implementing the presented method, analyzing the results, and writing the manuscript.

Corresponding author

Correspondence to Ali Harounabadi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sayari, S., Harounabadi, A. & Banirostam, T. Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern mining. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06178-7

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06178-7

Keywords

Search

Navigation