Multi-relational dynamic graph representation learning

In recent years, many dynamic graph representation learning methods have emerged due to the ubiquity of dynamic graph networks, such as social networks, medical networks, citation networks and traffic networks. Many researchers only consider the unitary relational topological information in a dynamic graph. However, real dynamic networks contain a large amount of multi-relational topological information. For example, there are different interactive relations such as sending a message, adding a friend, making a phone call, and sending an email in a social network, and they have different effects on node representation and should be distinguished. In addition, the non-topological information of nodes plays an important role in the node representation. Although these two types of information have been shown to improve the performance of many dynamic graph tasks, existing dynamic graph representation learning models could not integrate them well. Therefore, in this paper, we propose MRDGNN, a Multi-Relational Dynamic Graph Neural Network model, which can capture the dynamic evolution under each relational topology in the graph through a temporal multi-relational topology updater, including the participation of multi-relational topological and non-topological information of nodes. These two kinds of information will be adaptively fused into the representation of nodes by a merger. MRDGNN is continuously updated with the evolution of dynamic graphs and is a real-time learnable representation learning framework. Finally, we validate the effectiveness of MRDGNN for link prediction and relation prediction on four real datasets.