Skip to main content

Advertisement

Log in

Community detection based on influential nodes in dynamic networks

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

Abstract

Communities in a network are groups of nodes that are more strongly connected to each other. This article proposes a novel method for community detection in dynamic networks, focusing on influential nodes and overlapping communities. The method, named community detection based on adaptive multi-centrality aggregation (CDAMA), tackles two key challenges identifying influential nodes and overlapping communities. CDAMA introduces the Adaptive multi-centrality aggregation (AMCA) approach to identify influential nodes. AMCA integrates multiple centrality measures. The adaptive overlap control and merging (AOC-CM) approach addresses overlapping communities. AOC-CM utilizes structural, temporal, and semantic factors to strategically merge communities while preserving those with minimal overlap. CDAMA consists of five phases: receiving network snapshots, selecting influential nodes, launching communities, checking overlap and merging communities, and updating communities. Evaluation on three benchmark datasets demonstrates that CDAMA outperforms existing state-of-th-art methods in terms of Newman modularity, Modularity with split penalty and density modularity and Execution time. This suggests CDAMA is a valuable tool for tasks like viral marketing, information diffusion analysis, and network resilience studies.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig.1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are available through: Cit-Hep Ph: https://snap.stanford.edu/data/cit-HepPh.html. Cit—Hep Th: https://snap.stanford.edu/data/cit-HepTh.html. sx-mathoverflow: https://snap.stanford.edu/data/sx-mathoverflow.html. CollegeMsg: https://snap.stanford.edu/data/CollegeMsg.html.

References

  1. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174

    Article  MathSciNet  Google Scholar 

  2. Rossetti G, Cazabet R (2018) Community discovery in dynamic networks: a survey. ACM computing Surv CSUR 51(2):1–37

    Google Scholar 

  3. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35

    Article  Google Scholar 

  4. Ni Q, Guo J, Wu W, Wang H (2023) Influence-Based community partition with sandwich method for social networks. IEEE Trans Comput Soc Syst 10(2):819–830

    Article  Google Scholar 

  5. Long H, Li X, Liu X, Wang W (2023) “BBTA: detecting communities incrementally from dynamic networks based on tracking of backbones and bridges. Appl Intell 53(1):1084–1100

    Article  Google Scholar 

  6. Behera RK, Naik D, Rath SK, Dharavath R (2020) Genetic algorithm-based community detection in large-scale social networks. Neural Comput Appl 32(13):9649–9665

    Article  Google Scholar 

  7. Zhao X, Liang J, Wang J (2021) A community detection algorithm based on graph compression for large-scale social networks. Inf Sci 551:358–372

    Article  MathSciNet  Google Scholar 

  8. Roghani H, Bouyer A (2023) A fast local balanced label diffusion algorithm for community detection in social networks. IEEE Trans Knowl Data Eng 35:5472–5484

    Article  Google Scholar 

  9. Anjerani M Moeini A (2011) Selecting influential nodes for detected communities in real-world social networks. In: 2011 19th Iranian Conference on Electrical Engineering IEEE 1–6

  10. Su C, Wang Y, Zhang L (2014) Community detection in social networks based on influential nodes. J Softw 9(9):2409–2416

    Article  Google Scholar 

  11. Ahajjam S El Haddad M Badir H (2016) Influentials identification for community detection in complex networks. In: 2016 4th IEEE International Colloquium on Information Science and Technology (CIST) IEEE, 111–115

  12. Srinivas S, Rajendran C (2019) Community detection and influential node identification in complex networks using mathematical programming. Expert Syst Appl 135:296–312

    Article  Google Scholar 

  13. Ma T, Liu Q, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2020) LGIEM: global and local node influence based community detection. Future Gener Comput Syst 105:533–546

    Article  Google Scholar 

  14. Wang P, Huang Y, Tang F, Liu H, Lu Y (2021) [Retracted] overlapping community detection based on node importance and adjacency information. Secur Commun Netw 2021(1):8690662

    Google Scholar 

  15. Kumar S, Kumar A, Panda B (2022) Identifying influential nodes for smart enterprises using community structure with integrated feature ranking. IEEE Trans Industr Inf 19(1):703–711

    Article  MathSciNet  Google Scholar 

  16. Devi S and Rajalakshmi M (2023) “Community based influencer node identification using hybrid optimisation algorithm in social networks.” Journal of Experimental & Theoretical Artificial Intelligence 1–28

  17. Bhattacharya R, Nagwani NK, Tripathi S (2023) Detecting influential nodes with topological structure via graph neural network approach in social networks. Int J Inf Technol 15(4):2233–2246

    Google Scholar 

  18. Devi S, Rajalakshmi M (2023) Community detection by node betweenness using optimized girvan-newman cuckoo search algorithm. Inf Technol Control 52(1):53–67

    Article  Google Scholar 

  19. Gupta A, Khatri I, Choudhry A, Kumar S (2023) MCD: a modified community diversity approach for detecting influential nodes in social networks. J Intell Inf Syst 61(2):473–495

    Article  Google Scholar 

  20. Zheng H, Zhao H, Ahmadi G (2024) Towards improving community detection in complex networks using influential nodes. J Complex Netw 12:cnae001

    Article  MathSciNet  Google Scholar 

  21. Ma P and Meng F (2012)”A novel method for dynamic community discovery.” In: 2012 5th International Conference on Intelligent Computation Technology and Automation IEEE, 45–47

  22. Britz D Mai C Xu C, “Quantifying community growth in dynamic social networks.”

  23. Alvari H Hajibagheri A and Sukthankar G (2014)”Community detection in dynamic social networks: a game-theoretic approach,” In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) IEEE, 101–107

  24. Enugala R, Rajamani L, Ali K, Kurapati S (2015) Community detection in dynamic social networks: a survey. Int J Res Appl 2(6):278–285

    Google Scholar 

  25. Samie ME, Hamzeh A (2017) Community detection in dynamic social networks: a local evolutionary approach. J Inf Sci 43(5):615–634

    Article  Google Scholar 

  26. Fani H, Bagheri E (2017) Community detection in social networks. Encycl Semant Comput Robot Intell 1(01):1630001

    Article  Google Scholar 

  27. Guidi B, Michienzi A, Rossetti G (2019) Towards the dynamic community discovery in decentralized online social networks. J Grid Comput 17:23–44

    Article  Google Scholar 

  28. Djerbi R, Amad M, Imache R (2020) A new model for communities’ detection in dynamic social networks inspired from human families. Int J Int Technol Secur Trans 10(1–2):24–60

    Google Scholar 

  29. R Diestel, (2005) Graph theory 3rd ed. Graduate texts in mathematics, 12 173

  30. Banerjee S, Jenamani M, Pratihar DK (2020) A survey on influence maximization in a social network. Knowl Inf Syst 62(9):3417–3455

    Article  Google Scholar 

  31. Al-Garadi MA et al (2018) Analysis of online social network connections for identification of influential users: survey and open research issues. ACM Comput Surv CSUR 51(1):1–37

    Google Scholar 

  32. Khan BS and Niazi MA (2017) Network community detection: A review and visual survey. arXiv preprint arXiv:1708.00977

  33. Bahadori S, Zare H, Moradi P (2021) PODCD: probabilistic overlapping dynamic community detection. Expert Syst Appl 174:114650

    Article  Google Scholar 

  34. Chen N, Hu B, Rui Y (2020) Dynamic network community detection with coherent neighborhood propinquity. IEEE Access 8:27915–27926

    Article  Google Scholar 

  35. Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519(3):97–125

    Article  Google Scholar 

  36. Chen N, Liu Y, Cheng J, Liu Q (2018) A novel parallel community detection scheme based on label propagation. World Wide Web 21(5):1377–1398

    Article  Google Scholar 

  37. Jin D, Yu Z, Jiao P, Pan S, He D, Wu J, Zhang W (2021) A survey of community detection approaches: from statistical modeling to deep learning. IEEE Trans Knowl Data Eng 35(2):1149–1170

    Google Scholar 

  38. Cazabet R Rossetti G and Amblard F (2017) “Dynamic community detection.” ed

  39. Scripps J (2013) Discovering Influential Nodes in Social Networks through Community Finding. In WEBIST, 403–412

  40. Dai J et al (2019) Identifying influential nodes in complex networks based on local neighbor contribution. IEEE Access 7:131719–131731

    Article  Google Scholar 

  41. Leskovec J Kleinberg JM and Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In Knowledge Discovery and Data Mining

  42. Paranjape A Benson AR and Leskovec J (2017) Motifs in Temporal Networks. presented at the Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, United Kingdom, Available. https://doi.org/10.1145/3018661.3018731

  43. Panzarasa P, Opsahl T, Carley KM (2009) Patterns and dynamics of users’ behavior and interaction: Network analysis of an online community. J Am Soc Inform Sci Technol 60(5):911–932

    Article  Google Scholar 

  44. Cordeiro M, Sarmento RP, Gama J (2016) Dynamic community detection in evolving networks using locality modularity optimization. Soc Netw Anal Min 6(1):1–20

    Article  Google Scholar 

  45. Zhuang D, Chang MJ, Li M (2019) Dynamo: dynamic community detection by incrementally maximizing modularity. IEEE Trans Knowl Data Eng 33:1945

    Google Scholar 

  46. Su X, Cheng J, Yang H, Leng M, Zhang W, Chen X (2020) IncNSA: detecting communities incrementally from time-evolving networks based on node similarity. Int J Mod Phys C 31(07):2050094

    Article  MathSciNet  Google Scholar 

  47. Liu F Wu F Zhou C, and Yang J (2019) Evolutionary community detection in dynamic social networks. In: 2019 International Joint Conference on Neural Networks (IJCNN) IEEE, 1–7

  48. Seyedi SA, Lotfi A, Moradi P, Qader NN (2019) Dynamic graph-based label propagation for density peaks clustering. Expert Syst Appl 115:314–328

    Article  Google Scholar 

  49. Safdari H, Contisciani M, De Bacco C (2022) Reciprocity, community detection, and link prediction in dynamic networks. J Phys Compl 3(1):015010

    Article  Google Scholar 

  50. Kumar S, Mallik A, Sengar SS (2023) Community detection in complex networks using stacked autoencoders and crow search algorithm. J Supercomput 79(3):3329–3356

    Article  Google Scholar 

Download references

Funding

There is no funding.

Author information

Authors and Affiliations

Authors

Contributions

Marjan Mokhtari conceptualized the main research question and conducted a literature review on existing community detection methods and influence maximization techniques and drafted the background and related work sections of the manuscript. Meimanat Dadras formulated the mathematical framework for identifying influential nodes in dynamic networks and developed and implemented the proposed community detection algorithm based on influential nodes. Mahdi Kherad provided guidance and expertise in dynamic network analysis and performed the experiments and analyzed the results, including data collection and visualization and edited the manuscript, ensuring clarity and scientific rigor.

Corresponding author

Correspondence to Marjan Mokhtari.

Ethics declarations

Conflict of interests

The authors declare that there is no conflict of interest

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

Kherad, M., dadras, M. & Mokhtari, M. Community detection based on influential nodes in dynamic networks. J Supercomput 80, 24664–24688 (2024). https://doi.org/10.1007/s11227-024-06367-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-06367-4

Keywords