When to Remember Where You Came from: Node Representation Learning in Higher-order Networks

Abstract

For trajectory data that tend to have beyond first-order (i.e., non-Markovian) dependencies, higher-order networks have been shown to accurately capture details lost with the standard aggregate network representation. At the same time, representation learning has shown success on a wide range of network tasks, removing the need to hand-craft features for these tasks. In this work, we propose a node representation learning framework called EVO or Embedding Variable Orders, which captures non-Markovian dependencies by combining work on higher-order networks with work on node embeddings. We show that EVO outperforms baselines in tasks where high-order dependencies are likely to matter, demonstrating the benefits of considering high-order dependencies in node embeddings. We also provide insights into when it does or does not help to capture these dependencies. To the best of our knowledge, this is the first work on representation learning for higher-order networks.