🤖 AI Summary
This work addresses the challenge of modeling dynamic community evolution—encompassing community splitting, merging, and node additions or deletions—by proposing a novel generative temporal network model. The approach introduces, for the first time, a mutual information–based similarity measure to guide genetic search, thereby explicitly controlling the evolution of community structure across network snapshots. It further incorporates dynamic edge-generation probabilities conditioned on intra- and inter-community connectivity. The model jointly captures both the temporal evolution of communities and changes in node membership. Experimental results demonstrate that the framework effectively reproduces real-world dynamic community behaviors and successfully quantifies how node insertion and deletion rates influence the performance of dynamic community detection algorithms.
📝 Abstract
This paper introduces a generative model for temporal networks that jointly controls community evolution and dynamic node sets. The model represents community structure as a sequence of partitions and uses a genetic search guided by a similarity measure based on mutual information to regulate changes between snapshots. This allows explicit control of community evolution including splits and merges while handling node additions and removals. Temporal edges are then generated using intra- and inter-community probabilities derived from data or theoretical bounds to ensure connectivity. Simulation experiments on real-world datasets demonstrate the ability of the generative model to model the evolution of real dynamic communities. The model is used as a benchmark to study the impact of the rate at which nodes join/leave the network on the performance of dynamic community detection algorithms.