  Learning node representations on temporal graphs is a fundamental step to
learn real-word dynamic graphs efficiently. Real-world graphs have the nature
of continuously evolving over time, such as changing edges weights, removing
and adding nodes and appearing and disappearing of edges, while previous graph
representation learning methods focused generally on static graphs. We present
ConvDySAT as an enhancement of DySAT, one of the state-of-the-art dynamic
methods, by augmenting convolution neural networks with the self-attention
mechanism, the employed method in DySAT to express the structural and temporal
evolution. We conducted single-step link prediction on a communication network
and rating network, Experimental results show significant performance gains for
ConvDySAT over various state-of-the-art methods.
