Murphy Yuezhen Niu, Isaac L. Chuang, et al.
arXiv
Many irregular domains such as social networks, financial transactions, neuronconnections, and natural language structures are represented as graphs. A varietyof graph neural networks (GNNs) have been successfully applied for representa-tion learning and prediction on such graphs. However, in many of the applica-tions, the underlying graph changes over time and existing GNNs are inadequatefor handling such time varying graphs. In this paper we propose a novel techniquefor learning embeddings of time varying graphs based on a tensor framework. Themethod extends the popular graph convolutional network (GCN) for learning rep-resentations of time varying graphs using the recently proposed tensor M-producttechnique. Numerical experiments on four real datasets demonstrate that our pro-posed method outperforms a baseline method when used for edge classification.
Murphy Yuezhen Niu, Isaac L. Chuang, et al.
arXiv
Chao Yang, Xiaojian Ma, et al.
NeurIPS 2019
Florian Scheidegger, Luca Benini, et al.
NeurIPS 2019
Elizabeth Newman, Lior Horesh, et al.
arXiv