Graph Convolutional Network Representation Learning Method with Community-enhanced Temporal Features for Dynamic Graphs

Dynamic graph representation learning aims at learning the low-dimensional vector representation of nodes in dynamic networks, and the learned network representation can be used for various dynamic network analysis tasks.In dynamic networks, it is very important to accurately model time information, but most embedding methods completely ignore it, or use a series of static snapshots to approximate dynamic networks, which will lose a lot of important time information. Besides, community structure is an important feature of complex networks, and capturing network community structure during dynamic network representation learning has a positive impact on learning outcomes. In this paper, we propose a dynamic graph neural network representation learning method with Community-enhanced temporal features, the TCGCN. TCGCN introduces the community structure information of the dynamic network into the extracted temporal features, and then integrates the temporal features into the method based on graph convolution network to learn the representation of the nodes, so that the learning results can keep time dependence, and can maintain the community structure and global network structure features, comprehensive learning to the change of the dynamic network rule. The experimental results show that TCGCN can greatly improve the quality of dynamic network representation learning.