Transcription of node2vec: Scalable Feature Learning for Networks
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node2vec: Scalable Feature Learning for Networks
node2vec : Scalable Feature Learning for NetworksAditya GroverStanford LeskovecStanford tasks over nodes and edges in Networks require carefuleffort in engineering features used by Learning algorithms. Recentresearch in the broader field of representation Learning has led tosignificant progress in automating prediction by Learning the fea-tures themselves. However, present Feature Learning approachesare not expressive enough to capture the diversity of connectivitypatterns observed in we proposenode2vec, an algorithmic framework for learn-ing continuous Feature representations for nodes in Networks . Innode2vec, we learn a mapping of nodes to a low-dimensional spaceof features that maximizes the likelihood of preserving networkneighborhoods of nodes.
node2vec: Scalable Feature Learning for Networks Aditya Grover Stanford University adityag@cs.stanford.edu Jure Leskovec Stanford University jure@cs.stanford.edu
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