  This work investigates neural algorithmic reasoning to develop neural
networks capable of learning from classical algorithms. The main challenge is
to develop graph neural networks that are expressive enough to predict the
given algorithm outputs while generalizing well to out-of-distribution data. In
this work, we introduce a new graph neural network layer called Triplet Edge
Attention (TEA), an edge-aware graph attention layer. Our algorithm works by
precisely computing edge latent, aggregating multiple triplet messages using
edge-based attention. We empirically validate our TEA layer in the CLRS
benchmark and demonstrate a $5%$ improvement on average. In particular, we
achieve a $30%$ improvement for the string algorithms compared to the
state-of-the-art model.
