MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

MetaHGNIE is a novel framework for node importance estimation (NIE) in heterogeneous knowledge graphs (HKGs). It addresses limitations of existing methods by explicitly modeling higher-order dependencies and integrating structural and semantic information. The framework constructs a higher-order knowledge graph using meta-path sequences, where typed hyperedges capture multi-entity relational contexts. It employs a dual-channel architecture: a structure-aware hypergraph attention network for structural dependencies and a hypergraph transformer with sparse chunking for semantic representations. A multimodal fusion module then integrates these embeddings using contrastive learning and auxiliary supervision. Experiments on benchmark NIE datasets like FB15K, TMDB5K, MUSIC10K, and IMDB demonstrate MetaHGNIE's superior performance, achieving, for example, a Spearman correlation of 0.762 and NDCG@100 of 0.896 on TMDB5K, outperforming state-of-the-art baselines. Its sparse-chunked aggregation also reduces runtime by over 30% and GPU memory usage by up to 84.7% on FB15K.

Key takeaway

For Machine Learning Engineers developing recommendation systems or knowledge reasoning applications, MetaHGNIE offers a robust approach to node importance estimation. You should consider adopting its meta-path induced hypergraph modeling to capture higher-order dependencies, which significantly outperforms traditional pairwise methods. Implement its dual-channel architecture with sparse-chunked attention to efficiently integrate structural and semantic information, especially for large-scale heterogeneous knowledge graphs. This framework provides superior ranking performance and improved scalability.

Key insights

MetaHGNIE enhances node importance estimation in HKGs by disentangling and aligning higher-order structural and semantic information via hypergraph contrastive learning.

Principles

• Higher-order interactions improve node importance estimation.
• Disentangling structural and semantic signals is crucial.
• Cross-modal alignment enhances representation consistency.

Method

MetaHGNIE constructs meta-path induced hypergraphs, uses a dual-channel architecture (HGAT for structure, sparse-chunked transformer for semantics), and fuses embeddings via contrastive learning with auxiliary supervision.

In practice

• Use meta-paths to model multi-entity interactions.
• Apply sparse-chunked attention for large hypergraphs.
• Integrate structural and semantic embeddings with contrastive loss.

Topics

• Heterogeneous Knowledge Graphs
• Hypergraph Neural Networks
• Node Importance Estimation
• Contrastive Learning
• Sparse-Chunked Attention
• Multimodal Fusion

Code references

• SEU-WENJIA/DualHNIE

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.