  Graph Attention Networks (GATs) have been intensively studied and widely used
in graph data learning tasks. Existing GATs generally adopt the self-attention
mechanism to conduct graph edge attention learning, requiring expensive
computation. It is known that Spiking Neural Networks (SNNs) can perform
inexpensive computation by transmitting the input signal data into discrete
spike trains and can also return sparse outputs. Inspired by the merits of
SNNs, in this work, we propose a novel Graph Spiking Attention Network (GSAT)
for graph data representation and learning. In contrast to self-attention
mechanism in existing GATs, the proposed GSAT adopts a SNN module architecture
which is obvious energy-efficient. Moreover, GSAT can return sparse attention
coefficients in natural and thus can perform feature aggregation on the
selective neighbors which makes GSAT perform robustly w.r.t graph edge noises.
Experimental results on several datasets demonstrate the effectiveness, energy
efficiency and robustness of the proposed GSAT model.
