Continuous-Time Dynamic Interaction Network Learning Based on Evolutionary Expectation

Dynamic networks, such as social networks and recommendation systems, are widespread in the real world. Graph representation learning has emerged as an effective strategy for analyzing such networks. However, the problem lies in the fact that many current techniques treat dynamic networks as static or discrete structures, while continuous-time approaches often lack the capabilities to effectively handle networks with low node repetition behavior. To alleviate these problems, we first treat dynamic networks as continuous-time interactions and then propose a novel method for the transductive interaction prediction task. Our approach incorporates two key aspects: 1) evolutionary expectation learning and 2) temporal dynamic learning. The former imparts guidance to the network’s evolution and endows node embedding with a more profound wealth of information, while the latter provides detailed insight into the intricate process of network evolution. Together, these two components provide a comprehensive understanding of network behavior and can efficiently handle dynamic networks with low node repetition behavior. Specifically, we utilize an asynchronous training process, starting with the training of a multievent embedding module that captures information about the evolutionary expectation of dynamic networks. Based on this foundation, we train a temporal multievent embedding module to map the network’s dynamic evolution onto node embedding representations. Furthermore, we design a temporal single-event module that effectively captures implicit long-term interaction dependencies of nodes. To evaluate the effectiveness of our proposed method, we evaluate its performance on four data sets and demonstrate its superior performance compared to the baselines.