MODEC — Modeling and Detecting Evolutions of Communities


  • Mansoureh Takaffoli University of Alberta
  • Farzad Sangi University of Alberta
  • Justin Fagnan University of Alberta
  • Osmar Zaiane University of Alberta


Social network analysis encompasses the study of networked data and examines questions related to structures and patterns that can lead to the understanding of the data and the intrinsic relationships, such as identifying influential nodes, recognizing critical paths, predicting unobserved relationships, discovering communities, etc. All of these analyses, germane to a variety of application domains, are typically done on static information networks; that is, a fixed snapshot of the information network. Yet, a social network changes and understanding the evolution of the network and detecting these changes in the underlying structures is paramount for a multitude of applications. Looking at networks as fixed snapshots misses the opportunity to capture the evolutionary patterns. In this paper, we present a framework for modeling community evolution in social networks by tracking of events related to the life cycle of a community. We illustrate the capabilities of our framework by applying it to real datasets and validate the results using topics extracted from the tracked communities.




How to Cite

Takaffoli, M., Sangi, F., Fagnan, J., & Zaiane, O. (2021). MODEC — Modeling and Detecting Evolutions of Communities. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 626-629. Retrieved from