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Learning node representations on temporal graphs is a fundamental step tolearn real-word dynamic graphs efficiently. Real-world graphs have the natureof continuously evolving over time, such as changing edges weights, removingand adding nodes and appearing and disappearing of edges, while previous graphrepresentation learning methods focused generally on static graphs. We presentConvDySAT as an enhancement of DySAT, one of the state-of-the-art dynamicmethods, by augmenting convolution neural networks with the self-attentionmechanism, the employed method in DySAT to express the structural and temporalevolution. We conducted single-step link prediction on a communication networkand rating network, Experimental results show significant performance gains forConvDySAT over various state-of-the-art methods.

ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks