Automated Heterogeneous Network learning with Non-Recursive Message Passing

TL;DR

This paper introduces AutoGNR, a novel non-recursive message passing framework for heterogeneous information networks that improves performance by reducing noise and automatically optimizing GNN structures through neural architecture search.

Contribution

The paper proposes a non-recursive message passing mechanism and an automatic neural architecture search method tailored for heterogeneous networks, addressing key limitations of existing GNN approaches.

Findings

AutoGNR outperforms state-of-the-art methods on real-world HIN datasets.

The non-recursive approach reduces noise from uncorrelated node types.

Automatic architecture search efficiently finds optimal heterogeneous aggregation paths.

Abstract

Heterogeneous information networks (HINs) can be used to model various real-world systems. As HINs consist of multiple types of nodes, edges, and node features, it is nontrivial to directly apply graph neural network (GNN) techniques in heterogeneous cases. There are two remaining major challenges. First, homogeneous message passing in a recursive manner neglects the distinct types of nodes and edges in different hops, leading to unnecessary information mixing. This often results in the incorporation of ``noise'' from uncorrelated intermediate neighbors, thereby degrading performance. Second, feature learning should be handled differently for different types, which is challenging especially when the type sizes are large. To bridge this gap, we develop a novel framework - AutoGNR, to directly utilize and automatically extract effective heterogeneous information. Instead of recursive homogeneous message passing, we introduce a non-recursive message passing mechanism for GNN to mitigate noise from uncorrelated node types in HINs. Furthermore, under the non-recursive framework, we manage to efficiently perform neural architecture search for an optimal GNN structure in a differentiable way, which can automatically define the heterogeneous paths for aggregation. Our tailored search space encompasses more effective candidates while maintaining a tractable size. Experiments show that AutoGNR consistently outperforms state-of-the-art methods on both normal and large scale real-world HIN datasets.