Heterogeneous Graph Adaptive Flow Network

Abstract

Many graphs or networks are heterogeneous by nature, involving various vertex types and relation types. Most graph learning models for heterogeneous graphs employ meta-paths to guide neighbor selections and extract composite relations. However, the use of meta-paths to generate relations between the same vertex types may result in directed edges and failure to fully utilize the other vertex or edge types in the data. To address such a limitation, we propose Heterogeneous graph adaptive flow network (HetaFlow), which removes the need for meta-paths. HetaFlow decomposes the heterogeneous graph into flows and performs convolution across heterogeneous vertex and edge types, using an adaptation to change the vertex features based on the corresponding vertex and edge types during aggregation. Experiments on real-world datasets for vertex clustering and vertex classification demonstrate that HetaFlow outperforms other benchmark models and achieves state-of-the-art performance on commonly used benchmark datasets. The codes are available at https://github.com/AnonymizedC/HetaFlow.