Community detection on time-evolving graphs
Matteo Bianco
Community detection on time-evolving graphs.
Rel. Luca Cagliero, Luca Vassio. Politecnico di Torino, Master of science program in Mathematical Engineering, 2024
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) | Preview |
Abstract
Community detection in graphs is a fundamental task in network analysis, aimed at identifying clusters of densely connected nodes. It has applications in many fields, such as social media, biological systems and cybersecurity. While traditional methods focus on static graphs, many real-world networks are dynamic, with evolving structures and community memberships. This thesis addresses the challenge of dynamic community detection, proposing a semi-supervised approach. In this context, semi-supervision means leveraging both graph structure and knowledge of part of ground truth communities in order to infer those in the remaining part of the graph. Therefore, in a dynamic setting, we have a graph evolving for many timesteps and, in each time instant, we know part of the ground-truth communities.
Though the real communities we are informed of may differ from time to time, the addition of this information helps improving accuracy and robustness of algorithms
Publication type
URI
![]() |
Modify record (reserved for operators) |
