TdGraphEmbed: Temporal Dynamic Graph-Level Embedding

Abstract

Temporal dynamic graphs are graphs whose topology evolves over time, with nodes and edges added and removed between different time snapshots. Embedding such graphs in a low-dimensional space is important for a variety of tasks, including graphs' similarities, time series trends analysis and anomaly detection, graph visualization, graph classification, and clustering. Despite the importance of the temporal element in these tasks, existing graph embedding methods focus on capturing the graph's nodes in a static mode and/or do not model the graph in its entirety in temporal dynamic mode. In this study, we present tdGraphEmbed, a novel temporal graph-level embedding approach that extend the random-walk based node embedding methods to globally embed both the nodes of the graph and its representation at each time step, thus creating representation of the entire graph at each step. Our approach was applied to graph similarity ranking, temporal anomaly detection, trend analysis, and graph visualizations tasks, where we leverage our temporal embedding in a fast and scalable way for each of the tasks. An evaluation of tdGraphEmbed on five real-world datasets shows that our approach can outperform state-of-the-art approaches used for graph embedding and node embedding in temporal graphs.