EWCC Community Discovery Algorithm for Two-Layer Network

Abstract

Aiming at the problem of community discovery in relational networks, considering the strength of interaction between nodes and information seepage mechanism, an edge weight and connected component (EWCC) community discovery algorithm based on edge weight and connected branches is innovatively proposed.In order to verify effectiveness of the algorithm, firstly, five kinds of interactive two-layer network models are constructed.By analyzing influence of interaction degree of nodes between layers on the network topology, 30 data sets generated under five kinds of two-layer network models are determined.Secondly, the real data set is selected to compare with GN algorithm and KL algorithm in the evaluation criteria of modularity, algorithm complexity and community division number.Experimental results show that EWCC algorithm has high accuracy.Then, the numerical simulation shows that with the weakening of interaction relationship between layers, the module degree is inversely proportional to number of communities, and the community division effect is better when node relationship between layers is weaker.Finally, as an application of the algorithm, the “user-APP” two-layer network is constructed based on empirical data, and the community is divided.

Keywords

• relational network|community discovery|two-layer network|edge weight|connected branch