Dynamic Joint Variational Graph Autoencoders

Abstract

Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for static graphs mainly and cannot capture the evolution of a large dynamic network. In this paper, we propose Dynamic joint Variational Graph Autoencoders (Dyn-VGAE) that can learn both local structures and temporal evolutionary patterns in a dynamic network. Dyn-VGAE provides a joint learning framework for computing temporal representations of all graph snapshots simultaneously. Each auto-encoder embeds a graph snapshot based on its local structure and can also learn temporal dependencies by collaborating with other autoencoders. We conduct experimental studies on dynamic real-world graph datasets and the results demonstrate the effectiveness of the proposed method.

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• Graph Embedding Techniques, Applications, and Performance: A Survey
• Variational Graph Auto-Encoders
• DynGEM: Deep Embedding Method for Dynamic Graphs
• Representation Learning over Dynamic Graphs
• Learning Dynamic Embeddings from Temporal Interactions
• DynamicGEM: A Library for Dynamic Graph Embedding Methods