Edge classification based on Convolutional Neural Networks for community detection in complex network

Community detection is a fundamental problem for many networks, and many methods have been proposed to resolve it. However, due to rapid increases in the scale and diversity of networks, the modular organization at the global level in many large networks is often extremely difficult to recognize. In this paper, we propose a new method based on deep learning on ground-truth communities, with the aim of revealing community structure in large real-world networks. The contributions of this paper are 1) proposing an edge-to-image (E2I) model that can transfer the edge structure to an image structure; 2) construction of a community network (ComNet) to classify the two types of edges, which are those in the same community and others between different communities; 3) making it easier to obtain local views of network communities by breadth-first search based on edge classification; and 4) merging preliminary communities with local modularity R, making it easy to optimize the community structure and obtain the final community structure of given networks. The experimental results show that the proposed edge classification method based on deep convolution neural networks can increase the accuracy of community structure evaluation compared with the existing methods in computer-generated networks and large-scale real-world networks.