HetTree: Heterogeneous Tree Graph Neural Network
Mingyu Guan Jack W. Stokes Qinlong Luo Fuchen Liu Purvanshi Mehta Elnaz Nouri Taesoo Kim
The recent past has seen an increasing interest in Heterogeneous Graph Neural
Networks (HGNNs) since many real-world graphs are heterogeneous in nature, from
citation graphs to email graphs. However, existing methods ignore a tree
hierarchy among metapaths, which is naturally constituted by different node
types and relation types. In this paper, we present HetTree, a novel
heterogeneous tree graph neural network that models both the graph structure
and heterogeneous aspects in a scalable and effective manner. Specifically,
HetTree builds a semantic tree data structure to capture the hierarchy among
metapaths. Existing tree encoding techniques aggregate children nodes by
weighting the contribution of children nodes based on similarity to the parent
node. However, we find that this tree encoding fails to capture the entire
parent-children hierarchy by only considering the parent node. Hence, HetTree
uses a novel subtree attention mechanism to emphasize metapaths that are more
helpful in encoding parent-children relationships. Moreover, instead of
separating feature learning from label learning or treating features and labels
equally by projecting them to the same latent space, HetTree proposes to match
them carefully based on corresponding metapaths, which provides more accurate
and richer information between node features and labels. Our evaluation of
HetTree on a variety of real-world datasets demonstrates that it outperforms
all existing baselines on open benchmarks and efficiently scales to large
real-world graphs with millions of nodes and edges.