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arXiv · 1910.01963

Dynamic Joint Variational Graph Autoencoders

Sedigheh Mahdavi·Shima Khoshraftar·Aijun An
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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.

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Sedigheh Mahdavi, Shima Khoshraftar, Aijun An. 2019-10-04. Dynamic Joint Variational Graph Autoencoders. https://arxiv.org/abs/1910.01963

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