Community Detection in Temporal Networks with Dynamical Differential Equations

Community detection in temporal networks has been a hot topic in recent years. In this paper, a dynamical differential equations network model is proposed to imitate the consistently changing states of nodes in the network. This paper not only discussed positive temporal positive networks and considered signed temporal networks. During the interaction, each node will update its state based on the differential equations. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. There exists an intuition about the nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other with time. Communities are detected ultimately when the states of nodes are stable. Experiments on real world show the efficiency of detection performance. The stable states reveal community structure of the detected networks successfully.