AM-HGCN: An Adaptive Multi-Head Hypergraph Convolutional Network for Few-Shot Regression

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AM-HGCN: An Adaptive Multi-Head Hypergraph Convolutional Network for Few-Shot Regression

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[1]WangMei,YAN Zujia,GAO Yatian,et al.AM-HGCN: An Adaptive Multi-Head Hypergraph Convolutional Network for Few-Shot Regression[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[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: 48 Number of periods: 2027 XX Page number: 1-8 Column: Public date: 2027-12-10

Title:: AM-HGCN: An Adaptive Multi-Head Hypergraph Convolutional Network for Few-Shot Regression

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

Keywords:: Few- Shot Learning; Hypergraph; Hypergraph Convolutional Neural Network; Multi head attention mechanism; Meta le

CLC:: TP301. 6O 1-0

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

Abstract:: High-order interactions among multiple nodes were difficult to be captured by graph neural networks based on binary-edge structures, and static graph topologies were unable to adapt 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 which 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, 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, and 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 MiniImageNet datasets, and for structured datasets, AM-HGCN was observed to outperform mainstream baseline models significantly in evaluation metrics, with the coefficient of determination (R^2) improved by up to 1.1%, validating the model’s effectiveness in capturing complex relationships. Significance tests yielded a p-value of 0.04, statistically confirming the reliability of this improvement, and 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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