Universal Self-Attention Network for Graph Classification

Existing graph neural network-based models have mainly been biased towards a supervised training setting; and they often share the common limitations in exploiting potential dependencies among nodes. To this end, we present U2GNN, a novel embedding model leveraging on the strength of the recently introduced universal self-attention network (Dehghaniet al., 2019), to learn low-dimensional embeddings of graphs which can be used for graph classification. In particular, given an input graph, U2GNN first applies a self-attention computation, which is then followed by a recurrent transition to iteratively memorize its attention on vector representations of each node and its neighbors across each iteration. Thus, U2GNN can address the limitations in the existing models to produce plausible node embeddings whose sum is the final embedding of the whole graph. Experimental results in both supervised and unsupervised training settings show that our U2GNN produces new state-of-the-art performances on a range of well-known benchmark datasets for the graph classification task. To the best of our knowledge, this is the first work showing that a unsupervised model can significantly work better than supervised models by a large margin.