Parameter-Free Hypergraph Neural Network for Few-Shot Node Classification

TL;DR

This paper introduces ZEN, a parameter-free hypergraph neural network that is fully linear, scalable, interpretable, and outperforms existing models in few-shot node classification tasks.

Contribution

ZEN is a novel, fully linear, parameter-free hypergraph neural network that achieves high accuracy and efficiency without complex training.

Findings

Outperforms 8 baseline models in accuracy

Achieves up to 696x speedup

Provides fully interpretable decision process

Abstract

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.