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Heterogeneous Graph Neural Network

Heterogeneous Graph Neural NetworkChuxu ZhangUniversity of Notre SongNEC Laboratories America, HuangUniversity of Notre Dame, JD SwamiUS Army Research V. ChawlaUniversity of Notre learning in Heterogeneous graphs aims to pursuea meaningful vector representation for each node so as to facili-tate downstream applications such as link prediction, personalizedrecommendation, node classification,etc. This task, however, ischallenging not only because of the demand to incorporate het-erogeneous structural ( Graph ) information consisting of multipletypes of nodes and edges, but also due to the need for consideringheterogeneous attributes or contents (e.д., text or image) associ-ated with each node.

DeepWalk [20], were initially developed to feed a set of short ran-dom walks over the graph to the SkipGram model [19] so as to approximate the node co-occurrence probability in these walks and obtain node embeddings. Subsequently, semantic-aware ap-proaches, e.д., metapath2vec [4], were proposed to address node

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