Abstract
Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems. However, most existing methods rely on pairwise message passing mechanisms that fail to capture higher-order interactions induced by meta-relational structures. Furthermore, structural topology and semantic attributes are typically entangled within a unified embedding space, obscuring their distinct inductive biases and limiting the discriminative capacity of learned importance representations.
To address these limitations, we propose DualHNIE, a principled dual-channel hypergraph learning framework for node importance estimation. DualHNIE first constructs a higher-order knowledge graph by forming typed hyperedges from meta-path sequences, enabling explicit modeling of higher-order relational patterns. It then introduces two complementary encoders: a structure-aware hypergraph attention network that performs locally normalized aggregation over meta-path–induced hyperedges to capture localized structural dependencies, and a sparse-chunked hypergraph transformer that captures global semantic interactions while maintaining scalable computation.
We further design a contrastive alignment mechanism with auxiliary supervision, ensuring cross-view consistency while preserving modality-specific representation. Extensive experiments on multiple benchmark datasets demonstrate that DualHNIE outperforms state-of-the-art methods, validating the effectiveness of explicit high-order modeling and disentangled dual-channel representation learning for heterogeneous knowledge graphs. Code and datasets are available at GitHub.
Blogger's Review: DualHNIE significantly enhances the estimation accuracy of node importance in heterogeneous knowledge graphs by introducing higher-order knowledge graph and dual-channel learning framework. This innovative approach in modeling structural and semantic interactions provides a new perspective for future graph learning research, making it worthy of attention.