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Graph embedding methods represent nodes in a continuous vector space,preserving information from the graph (e.g. by sampling random walks). Thereare many hyper-parameters to these methods (such as random walk length) whichhave to be manually tuned for every graph. In this paper, we replace randomwalk hyper-parameters with trainable parameters that we automatically learn viabackpropagation. In particular, we learn a novel attention model on the powerseries of the transition matrix, which guides the random walk to optimize anupstream objective. Unlike previous approaches to attention models, the methodthat we propose utilizes attention parameters exclusively on the data (e.g. onthe random walk), and not used by the model for inference. We experiment onlink prediction tasks, as we aim to produce embeddings that best-preserve thegraph structure, generalizing to unseen information. We improvestate-of-the-art on a comprehensive suite of real world datasets includingsocial, collaboration, and biological networks. Adding attention to randomwalks can reduce the error by 20% to 45% on datasets we attempted. Further, ourlearned attention parameters are different for every graph, and ourautomatically-found values agree with the optimal choice of hyper-parameter ifwe manually tune existing methods.

Watch Your Step: Learning Node Embeddings via Graph Attention