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Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification

Murong Yang, Shihui Ying, Yue Gao, Xin-Jian Xu

TL;DR

A novel framework, overlap-aware meta-learning attention for hypergraph neural networks (OMA-HGNN), that integrates both structural and feature similarities and demonstrates that OMA-HGNN excels in learning superior node representations and outperforms these baselines.

Abstract

Although hypergraph neural networks (HGNNs) have emerged as a powerful framework for analyzing complex datasets, their practical performance often remains limited. On one hand, existing networks typically employ a single type of attention mechanism, focusing on either structural or feature similarities during message passing. On the other hand, assuming that all nodes in current hypergraph models have the same level of overlap may lead to suboptimal generalization. To overcome these limitations, we propose a novel framework, overlap-aware meta-learning attention for hypergraph neural networks (OMA-HGNN). First, we introduce a hypergraph attention mechanism that integrates both structural and feature similarities. Specifically, we linearly combine their respective losses with weighted factors for the HGNN model. Second, we partition nodes into different tasks based on their diverse overlap levels and develop a multi-task Meta-Weight-Net (MWN) to determine the corresponding weighted factors. Third, we jointly train the internal MWN model with the losses from the external HGNN model and train the external model with the weighted factors from the internal model. To evaluate the effectiveness of OMA-HGNN, we conducted experiments on six real-world datasets and benchmarked its perfor-mance against nine state-of-the-art methods for node classification. The results demonstrate that OMA-HGNN excels in learning superior node representations and outperforms these baselines.

Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification

TL;DR

A novel framework, overlap-aware meta-learning attention for hypergraph neural networks (OMA-HGNN), that integrates both structural and feature similarities and demonstrates that OMA-HGNN excels in learning superior node representations and outperforms these baselines.

Abstract

Although hypergraph neural networks (HGNNs) have emerged as a powerful framework for analyzing complex datasets, their practical performance often remains limited. On one hand, existing networks typically employ a single type of attention mechanism, focusing on either structural or feature similarities during message passing. On the other hand, assuming that all nodes in current hypergraph models have the same level of overlap may lead to suboptimal generalization. To overcome these limitations, we propose a novel framework, overlap-aware meta-learning attention for hypergraph neural networks (OMA-HGNN). First, we introduce a hypergraph attention mechanism that integrates both structural and feature similarities. Specifically, we linearly combine their respective losses with weighted factors for the HGNN model. Second, we partition nodes into different tasks based on their diverse overlap levels and develop a multi-task Meta-Weight-Net (MWN) to determine the corresponding weighted factors. Third, we jointly train the internal MWN model with the losses from the external HGNN model and train the external model with the weighted factors from the internal model. To evaluate the effectiveness of OMA-HGNN, we conducted experiments on six real-world datasets and benchmarked its perfor-mance against nine state-of-the-art methods for node classification. The results demonstrate that OMA-HGNN excels in learning superior node representations and outperforms these baselines.

Paper Structure

This paper contains 30 sections, 4 theorems, 68 equations, 7 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related works
  3. HGNNs
  4. Message passing in HGNNs
  5. Meta-learning for sample weighting
  6. Adaptive weighting via multi-task meta-learning
  7. Preliminaries
  8. Hypergraphs
  9. Two attention mechanisms based on SS and FS
  10. Two-stage message passing on hypergraphs
  11. Meta-learning based on MWN
  12. The proposed method
  13. Integrating attention mechanisms for HGNNs
  14. Overlap-aware meta-learning attention
  15. Optimization procedure
  16. ...and 15 more sections

Key Result

Theorem 1

Suppose the loss functions $\ell_1$ and $\ell_2$ are Lipschitz smooth with constant $M$, and $\mathcal{V}(\cdot, \cdot; \Theta)$ is differentiable with a $\delta$-bounded gradient and twice differentiable with its Hessian bounded by $\mathcal{B}$. Additionally, assume that the gradients of $\ell_1$ in $\mathcal{O}(1/\epsilon^2)$ steps. More specifically, where $C$ is a constant independent of th

Figures (7)

  • Figure 1: Two-stage message passing process in the hypergraph
  • Figure 2: Kernel density estimation curves of sample losses from two attention mechanisms.
  • Figure 3: An example of a hypergraph, where $v_1$'s overlap level is $p_1=\frac{4+4+4}{7}=1.714$.
  • Figure 4: The architecture of MT-MWN, i.e., the internal model.
  • Figure 5: Schematic diagram of OMA-HGNN. The internal model is the overlap-aware Meta-Weight Net, and the external model is the attention-fused HGNN classifier.
  • ...and 2 more figures

Theorems & Definitions (7)

  • Theorem 1
  • Theorem 2
  • Proposition 1
  • Lemma 1
  • proof
  • proof
  • proof