Continuous-time graph directed information maximization for temporal network representation.

Given a temporal network that is composed of a stream of events that evolves continuously over time, like social posts on Reddit, how can we spot interesting events like anomalous posts? In general, it is critical to learn useful information from event time, attributes, and network structure, which means that the anomalous post can be detected by over-frequent posts, impolite comments, and incorrect choices of subreddits. In this paper, we propose a new learning objective, named Continuous-time Graph Directed Information Maximization (CGDIM), to learn informative node presentations for temporal networks. The proposed CGDIM is based on the popular mutual information maximization (InfoMax) method to learn node features that are shared across temporal neighborhoods. Specifically, to capture the time causal relations among edges with continuous time, we utilize directed information rather than mutual information as the measure. The designed learning objective is to maximize the directed information with the direction from the inputs of temporal neighborhoods to the node representations. By setting a source variable that only contains useful information on each node, we prove that directed information is more effective than mutual information for preserving information of the source variables. To show the effectiveness of the CGDIM, we train several popular backbone models for temporal network representation with the CGDIM and test the performance on temporal node/edge classification. Experiment results on four real-world temporal network datasets show that the proposed CGDIM can improve the Average-Precision scores of the backbone models by 3%-8%. [ABSTRACT FROM AUTHOR]