Heterogeneous Graph Neural Network

Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the demand to incorporate heterogeneous structural (graph) information consisting of multiple types of nodes and edges, but also due to the need for considering heterogeneous attributes or contents (e.g., text or image) associated with each node. Despite a substantial amount of effort has been made to homogeneous (or heterogeneous) graph embedding, attributed graph embedding as well as graph neural networks, few of them can…

• NS
  National Science Foundation

  Awards: IIS-1447795, 1447795, W911NF-09-2-0053
• AR
  Army Research Laboratory

  Awards: W911NF, W911NF-09-2-0053