2021/11/25 by Aurélio Ribeiro Costa, Costa, Aurélio Ribeiro
Computer Science · Decision Sciences · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Game Theory and Applications #I.2.6 #Machine Learning (cs.LG) #Opinion Dynamics and Social Influence #Social and Information Networks (cs.SI) #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2111.15623
arxiv created 2021/11/25 · openalex publication_date 2021/11/25 · arxiv updated 2021/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The identification of community structure in a social network is an important problem tackled in the literature of network analysis. There are many solutions to this problem using a static scenario, when facing a dynamic scenario some solutions may be adapted but others simply do not fit, moreover when considering the demand to analyze constantly growing networks. In this context, we propose an approach to the problem of community detection in dynamic networks based on a reinforcement learning strategy to deal with changes on big networks using a local optimization on the modularity score of the changed entities. An experiment using synthetic and real-world dynamic network data shows results comparable to static scenarios.