零参数模型ZEN在超图少样本节点分类中实现高效高准确率
Parameter-Free Hypergraph Neural Network for Few-Shot Node Classification
- 全线性无参设计，避免迭代训练和过拟合
- 11个真实数据集上准确率超越8个基线模型，最快快696倍
- 决策过程可解释，适合需要透明性的场景
超图上的少样本节点分类需在标签稀疏时仍能泛化并捕捉高阶结构。现有超图神经网络虽能有效编码此类结构，但常因复杂黑箱架构导致过拟合与扩展性差。本文提出ZEN（零参数超图神经网络），一种完全线性且无参数的模型，在保持表达力的同时兼顾效率。基于线性化超图神经网络的统一形式，ZEN引入可解析的闭式解求解权重矩阵，并采用冗余感知传播机制，避免迭代训练并消除冗余自信息。在11个真实超图基准上，ZEN持续优于8个基线模型，分类准确率更高，且最快比最快速对手快696倍。此外，ZEN的决策过程完全可解释，为数据集特性提供洞察。代码与数据集已开源于https://github.com/chaewoonbae/ZEN。
原文摘要 · Abstract (English)
Few-shot node classification on hypergraphs requires models that generalize from scarce labels while capturing high-order structures. Existing hypergraph neural networks (HNNs) effectively encode such structures but often suffer from overfitting and scalability issues due to complex, black-box architectures. In this work, we propose ZEN (Zero-Parameter Hypergraph Neural Network), a fully linear and parameter-free model that achieves both expressiveness and efficiency. Built upon a unified formulation of linearized HNNs, ZEN introduces a tractable closed-form solution for the weight matrix and a redundancy-aware propagation scheme to avoid iterative training and to eliminate redundant self information. On 11 real-world hypergraph benchmarks, ZEN consistently outperforms eight baseline models in classification accuracy while achieving up to 696x speedups over the fastest competitor. Moreover, the decision process of ZEN is fully interpretable, providing insights into the characteristic of a dataset. Our code and datasets are fully available at https://github.com/chaewoonbae/ZEN.
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