Automated Heterogeneous Network learning with Non-Recursive Message Passing
This work addresses challenges in modeling complex real-world systems like social or biological networks, representing an incremental improvement over existing heterogeneous GNN methods.
The paper tackles the problem of applying graph neural networks to heterogeneous information networks by addressing noise from uncorrelated node types and handling diverse feature types, resulting in AutoGNR, which outperforms state-of-the-art methods on real-world datasets.
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.