Community Detection Based on Graph Dynamical Systems with Asynchronous Runs

A community in a network is a group of nodes that are densely connected internally but sparsely connected externally. We propose a novel approach for detecting communities in networks based on graph dynamical systems (GDS), which are computation models for networks of interacting entities. We introduce the Propose-Select-Adjust framework - a GDS-based computation model for solving network problems, and demonstrate how this model may be used in community detection. The advantage of this approach is that computation is distributed to each node which asynchronously computes its own solution. This makes the method suitable for decentralised and dynamic networks.