Efficient-Dyn: Dynamic Graph Representation Learning via Event-based Temporal Sparse Attention Network. (arXiv:2201.01384v1 [cs.LG])

Static graph neural networks have been widely used in modeling and
representation learning of graph structure data. However, many real-world
problems, such as social networks, financial transactions, recommendation
systems, etc., are dynamic, that is, nodes and edges are added or deleted over
time. Therefore, in recent years, dynamic graph neural networks have received
more and more attention from researchers. In this work, we propose a novel
dynamic graph neural network, Efficient-Dyn. It adaptively encodes temporal
information into a sequence of …

arxiv attention event graph learning network