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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. Despite a substantial amount of effort hasbeen made to homogeneous (or Heterogeneous ) Graph embedding ,attributed Graph embedding as well as Graph Neural networks, fewof them can jointly consider Heterogeneous structural ( Graph ) infor-mation as well as Heterogeneous contents information of each nodeeffectively.

neural network architecture with two modules to aggregate feature information of those sampled neighboring nodes. The first module encodes “deep” feature interactions of heterogeneous contents and generates content embedding for each node. The second module aggregates content (attribute) embeddings of different neighboring

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