  Heterogeneous graphs have multiple node and edge types and are semantically
richer than homogeneous graphs. To learn such complex semantics, many graph
neural network approaches for heterogeneous graphs use metapaths to capture
multi-hop interactions between nodes. Typically, features from non-target nodes
are not incorporated into the learning procedure. However, there can be
nonlinear, high-order interactions involving multiple nodes or edges. In this
paper, we present Simplicial Graph Attention Network (SGAT), a simplicial
complex approach to represent such high-order interactions by placing features
from non-target nodes on the simplices. We then use attention mechanisms and
upper adjacencies to generate representations. We empirically demonstrate the
efficacy of our approach with node classification tasks on heterogeneous graph
datasets and further show SGAT's ability in extracting structural information
by employing random node features. Numerical experiments indicate that SGAT
performs better than other current state-of-the-art heterogeneous graph
learning methods.
