Dual-Force Community Evolution: Integrating Peer Influence and Interest Similarity in Social Networks

Bowen Guo

doi:10.54097/4xgg7d71

Authors

Bowen Guo

DOI:

https://doi.org/10.54097/4xgg7d71

Keywords:

Community Evolution, Louvain Algorithm, Social Network Analysis, Large Language Models (LLMs).

Abstract

Understanding how communities evolve requires accounting for both social influence and individual interests. In this paper, the Community Evolution Framework, which leverages the Louvain algorithm for initial community detection and extends it with a dynamic evolution mechanism was proposed. The framework integrates the peer influence and interest similarity through a tunable scoring function, allowing community memberships to be updated iteratively until convergence. To enhance interpretability, this paper furthers incorporate the large language models to generate thematic explanations of evolved communities based on interest distributions. The experiments conducted on a large-scale student social network dataset demonstrate that the proposed framework achieves strong modularity while capturing realistic membership reorganization patterns. The results highlight that balancing peer pressure and personal interests produces more cohesive yet adaptable communities. This work provides a quantitative and interpretable approach to studying the community evolution, with potential applications to broader the network science and the social computing tasks.

Downloads

Download data is not yet available.

References

[1] Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech Theory Exp. 2008; 2008 (10): P10008. doi: 10.1088/1742-5468/2008/10/P10008.

[2] Fortunato S. Community detection in graphs. Phys Rep. 2010; 486 (3-5): 75-174. doi: 10.1016/j.physrep.2009.11.002.

[3] Rossetti G, Cazabet R. Community discovery in dynamic networks: A survey. ACM Comput Surv. 2019; 51 (2): 1-37. doi:10.1145/3172867.

[4] Ahmad H, Sajid M, Mazhar F, Fuzail M. Mapping unseen connections: Graph clustering to expose user interaction patterns. J Future Artif Intell Technol. 2025; 1 (4): 474-96. doi: 10.62411/faith.3048-3719-77.

[5] Palla G, Barabási AL, Vicsek T. Quantifying social group evolution. Nature. 2007; 446 (7136): 664-7. doi: 10.1038/nature05670.

[6] Anuar SHH, Rahman NA, Abdullah R, et al. Bird flock effect-based dynamic community detection: Unravelling network patterns over time. Alex Eng J. 2025; 112: 177-208. doi: 10.1016/j.aej.2024.10.097.

[7] Hopcroft J, Khan O, Kulis B, Selman B. Tracking evolving communities in large linked networks. Proc Natl Acad Sci U S A. 2004; 101 Suppl 1: 5249-53. doi: 10.1073/pnas.0307750100.

[8] Asur S, Parthasarathy S, Ucar D. An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans Knowl Discov Data. 2009; 3 (4): 1-36. doi: 10.1145/1631162.1631164.

[9] Students’ social network profile clustering [Internet]. 2025 [cited 2025 Sep 20]. Available from: https://www.kaggle.com/datasets/zabihullah18/students-social-network-profile-clustering.

[10] Huang A. Similarity measures for text document clustering. InProceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand 2008 Apr 14 (Vol. 4, pp. 9-56).

[11] Zhou D, Bousquet O, Lal T, Weston J, Schölkopf B. Learning with local and global consistency. Advances in neural information processing systems. 2003; 16.

[12] Jain AK. Data clustering: 50 years beyond K-means. Pattern Recognit Lett. 2010; 31 (8): 651-66. doi: 10.1016/j.patrec.2009.09.011.

[13] RafieeFard J, Teimourpour B, Shaghouzi M, Jami M. A comparison of centrality measures for community evolution discovery. In: Proc 8th Int Conf Web Res (ICWR). Tehran, Iran: IEEE; 2022. p.183-8. doi: 10.1109/ICWR54782.2022.9786228.

[14] Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci U S A. 2006; 103 (23): 8577-82. doi: 10.1073/pnas.0601602103.

[15] Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L. Explaining explanations: An overview of interpretability of machine learning. In: Proc IEEE 5th Int Conf Data Sci Adv Anal (DSAA). Turin, Italy: IEEE; 2018. p.80-9. doi: 10.1109/DSAA.2018.00018.

[16] Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv: 1702.08608. 2017. doi: 10.48550/arXiv.1702.08608.

[17] Yang J, Leskovec J. Defining and evaluating network communities based on ground-truth. InProceedings of the ACM SIGKDD workshop on mining data semantics 2012 Aug 12 (pp. 1-8).