[1]王梅,闫祖嘉,高雅田,等.自适应多头超图卷积网络的小样本回归模型[J].郑州大学学报(工学版),2027,48(XX):1-8.[doi:10.13705/j. issn.1671-6833.2026.02.003]
　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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自适应多头超图卷积网络的小样本回归模型()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
48
期数:
2027年XX
页码:
1-8
栏目:
出版日期:
2027-12-10

文章信息/Info

Title:
AM-HGCN: An Adaptive Multi-Head Hypergraph Convolutional Network for Few-Shot Regression
作者:
王梅 1,2 , 闫祖嘉 1 , 高雅田 1,2 , 高俊涛1,2
1. 东北石油大学 计算机与信息技术学院, 黑龙江 大庆 163318;2. 黑龙江省石油大数据与智能分析重点实验室黑龙江,大庆 163318
Author(s):
WangMei 1,2 , YAN Zujia 1,2 , GAO Yatian 1,2 , GAO Juntao1,2
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
关键词:
小样本学习;  超图;  超图卷积神经网络;  多头注意力机制;  元学习
Keywords:
Few- Shot Learning;  Hypergraph;  Hypergraph Convolutional Neural Network;  Multi head attention mechanism;  Meta le
分类号:
TP301. 6O 1-0
DOI:
10.13705/j. issn.1671-6833.2026.02.003
文献标志码:
A
摘要:
针对图神经网络基于二元边结构难以捕捉多节点间的高阶交互，且固定拓扑的图结构无法适应动态数据分布。为此，提出一种基于自适应多头超图卷积网络的小样本回归模型（AM-HGCN）。首先，通过动态超图构建方法融合特征相似度与拓扑结构，结合k阶邻居（k-hop Neighbors）和k近邻（k-NN）策略生成多尺度超边，自适应捕捉特征间的交互关系；其次，设计多头超图卷积网络，利用并行注意力头提取异构特征，并通过动态门机制融合多粒度信息，增强模型的表达能力；最后，引入模型无关元学习框架，通过内外循环优化实现快速任务适应。在数据集 Boston Housing、Energy Efficiency、IMDB、MiniImageNet 上的实验表明：对于结构化数据集，AM-HGCN 在评价指标上显著优于主流基线模型，其中决定系数最高提升 1.1%，验证了其对复杂关系建模的有效性。显著性检验结果p值为 0.04，从统计学角度有力证实了这一提升的可靠性。消融实验进一步证明，动态超图与多头注意力机制的协同作用是小样本回归性能提升的关键，实验结果验证了本方法的有效性。
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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备注/Memo

备注/Memo:
收稿日期:2025-12-21;修订日期:2026-02-18
基金项目:国家自然科学基金项目( 51774090, 62076234) ;黑龙江省科技创新基地项目( JD24A009) ;黑龙江省自然科学基金项目( LH2024F005) ;黑龙江省博士后科研启动金资助项目( LBH-Q20080)
作者简介:王梅(1976— ) ,女,河北安国人,东北石油大学教授,博士,主要从事机器学习和智能油气田领域研究,E-mail:wangmei@nepu.edu.cn。
更新日期/Last Update: 2026-04-03