Few-shot Regression Model Based on Adaptive Multi-head Hypergraph Convolutional Network

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Few-shot Regression Model Based on Adaptive Multi-head Hypergraph Convolutional Network

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[1]WANG Mei,YAN Zujia,GAO Yatian,et al.Few-shot Regression Model Based on Adaptive Multi-head Hypergraph Convolutional Network[J].Journal of Zhengzhou University (Engineering Science),2026,47(5):85-92.[doi:10.13705/j.issn.1671-6833.2026.02.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 5 Page number: 85-92 Column: Public date: 2026-09-09

Title:: Few-shot Regression Model Based on Adaptive Multi-head Hypergraph Convolutional Network

Author(s):: WANG Mei^1,2, YAN Zujia¹, GAO Yatian^1,2, GAO Juntao^1,2; 1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China; 2. Heilongjiang Provincial Key Laboratory of Petroleum Big Data and Intelligent Analysis, Northeast Petroleum University, Daqing 163318, China

Keywords:: few-shot learning; hypergraph; hypergraph convolutional neural network; multi head attention mechanism; meta-learning

CLC:: TP301

DOI:: 10.13705/j.issn.1671-6833.2026.02.003

Abstract:: It was difficult to capture high‑order interactions among multiple nodes by graph neural networks based on binary‑edge structures, and static graph topologies were not adaptable to dynamic data distributions. To address these limitations, a few‑shot regression model based on adaptive multi‑head hypergraph convolutional networks (AM‑HGCN) was proposed. In this model, feature similarity and topological structure were integrated through a dynamic hypergraph construction method, multi‑scale hyperedges were generated using k\_hop neighbors and k-nearest neighbors (k\_NN) strategies to enable adaptive capture of feature interactions. Then, a multi‑head hypergraph convolutional network was designed to extract heterogeneous features via parallel attention heads and fuse multi‑granularity information through a dynamic gating mechanism to enhance expressive capability. Finally, a model‑agnostic meta‑learning framework was introduced to achieve rapid task adaptation through inner‑ and outer‑loop optimization. Experiments were conducted on the Boston Housing, Energy Efficiency, IMDB, and MinilmageNet datasets. For structured datasets, AM‑HGCN outperform mainstream baseline models significantly in evaluation metrics, The coefficient of determination was 0.775, which verifies its effectiveness in modeling complex relationships. Ablation studies further demonstrated that the collaborative effect of dynamic hypergraphs and multi‑head attention mechanisms was crucial for the enhancement of few‑shot regression performance, overall validating the effectiveness of the proposed method.

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Last Update: 2026-09-07