Watch Your Step: Learning Graph Embeddings Through Attention

  title={Watch Your Step: Learning Graph Embeddings Through Attention},
  author={Sami Abu-El-Haija and Bryan Perozzi and Rami Al-Rfou' and Alex Alemi},
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be manually tuned for every graph. In this paper, we replace random walk hyperparameters with trainable parameters that we automatically learn via backpropagation. In particular, we learn a novel attention model on the power series of the transition matrix, which… CONTINUE READING
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