node2vec: Scalable Feature Learning for Networks

KDD 2016
Volume: 2016, Pages: 855 - 864
Published: Aug 13, 2016
Abstract
Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose...
Paper Details
Title
node2vec: Scalable Feature Learning for Networks
Published Date
Aug 13, 2016
Journal
Volume
2016
Pages
855 - 864
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