Title: Parameter-Free Hypergraph Neural Network for Few-Shot Node Classification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Hypergraph neural network

Few-shot node classification

Linear model
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.


Peer reviews:
comments: SUMMARY:
The paper proposes a novel parameter-free hypergraph neural network for few-shot node classification. Tractable Closed-form Solution (TCS) eliminates iterative training, and Redundancy-Aware Propagation (RAP) explicitly removes self-information redundancy. Comprehensive tests show improved accuracy and efficiency compared to previous methods.

STRENGTHS AND WEAKNESSES:
**Strengths**
* The method shows improved accuracy and efficiency compared to previous methods.
* The paper is technically solid with rigorous theoretical analysis.
* The paper is well-written and easy to read.

**Weakness**
* There is no error bounds for closed-form approximation in Theorem 1.
* Table 4 presents values without specifying units. If the time for the proposed method is used as the unit of time, please state this clearly and maybe provide the original data in the appendix.
* Typos exist. For example, $W_*$ should be $W^*$ in Proof of lemma 1.

QUESTIONS:
* Can the authors provide error bounds for closed-form approximation in Theorem 1?
* Can the authors clarify the unit used in Table 4?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-24

comments: SUMMARY:
This paper proposes ZEN, which is a parameter-free hypergraph neural network. They theoretically derive their parameter-free solution. The experimental study shows the effectiveness and efficiency of ZEN compared with 8 baselines in ten datasets.

STRENGTHS AND WEAKNESSES:
Strong points.

S1. It is surprising that the parameter-free method ZEN outperforms hypergraph neural networks with learning parameters.

S2. Their theoretical analysis supports their structural design of ZEN.

S3. The experimental study is sufficient, validating efficiency and effectiveness in ten datasets, analyzing the effectiveness of components in ZEN as an ablation study, and testing interpretability.

Weak points.

W1. It is unclear why the authors focus only on few-shot node classification. ZEN seems distinct from the few-shot node classification. And, it is unclear what the performance is if the size of the training data varies.

W2. They do not show the actual running time in ZEN.

QUESTIONS:
Q1. Why do the authors focus on few-shot classifications? Is it possible to use ZEN for non-few-shot settings?

Q2. Could you add the actual running time in ZEN?

LIMITATIONS:
Yes, but very short.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-06-27

comments: SUMMARY:
This paper focuses on the few-shot node classification task on hypergraphs, and proposes a model, ZEN, to capture high-order relationships while maintaining strong generalization ability and scalability. Experiments on 11 real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

STRENGTHS AND WEAKNESSES:
S1. This paper presents a comprehensive study on liner hypergraph neural networks.

S2. Two techniques, tractable closed-form approximation and redundancy-aware propagation, are developed to improve the efficiency and the corresponding time complexity analysis is provided.

S3. Experiments on real-world datasets have been conducted and the results show the superiority of the proposed method.

W1. While the paper addresses the problem of few-shot node classification, it does not follow the conventional $n$-way $k$-shot learning paradigm in the evaluation setup. This deviation from standard practices should be discussed or justified.

W2. The ablation study shows that removing certain components of ZEN may lead to improved performance, which requires further discussion.

QUESTIONS:
Q1. More experiments are needed to evaluate the effectiveness of the proposed method under the $n$-way $k$-shot setting.

Q2. The ablation study (Table 6) shows that ZEN underperforms compared to the No TCS variant on the 20News and MN40 datasets, and compared to the No RAP variant on the Senate dataset. Thus, the limitations of ZEN need to be clarified in the revised manuscript.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ZEN (Zero-Parameter Hypergraph Neural Network), a linear and training-free model for few-shot node classification on hypergraphs. ZEN is built upon a unified linearized formulation of existing HNNs and introduces two key innovations: (1) a tractable closed-form solution (TCS) for computing the weight matrix without iterative optimization, and (2) a redundancy-aware propagation (RAP) mechanism to eliminate self-information in multi-hop neighborhoods. The method shows consistent performance improvements over eight strong baselines across 11 real-world datasets. The model is interpretable and the paper includes a detailed ablation and case study.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The formulation of a fully parameter-free HNN using closed-form solutions and a redundancy-aware propagation is both original and impactful, especially for low-resource, few-shot learning scenarios.\n2. The theoretical contributions, including the derivation of the closed-form approximation (Theorem 1) and its assumptions, are carefully motivated and reasonably justified.\n3. The theoretical contributions, including the derivation of the closed-form approximation (Theorem 1) and its assumptions, are carefully motivated and reasonably justified.\n\nWeaknesses:\n1. As acknowledged by the authors, ZEN is specifically designed for node classification. Extensions to other hypergraph tasks (e.g., hyperedge prediction, clustering) are left as future work. This means the scope of applicability of this work is limited. The authors may consider analyzing the effectiveness of the work in other tasks.\n2. The paper discusses the success of linear GNNs such as SGC and APPNP, but does not directly compare ZEN to their hypergraph counterparts or generalizations. A direct comparison with a naïve linearized hypergraph model (beyond HGNN) would provide a stronger baseline.\n3. ZEN is limited to 2-hop propagation. While this is justified by computational cost and redundancy, it may restrict representational power on deeper hypergraph structures. A discussion on possible approximations or sampling strategies for deeper propagation would be a valuable addition.\n4. Lack of baselines. The authors focus on the task of few-shot node classification. However, they only conduct experiments with hypergraph neural networks (HNN) models. There are multiple few-shot node classification works that can act as baselines.\n\nQUESTIONS:\nDid the authors consider involving more baselines for evaluation?\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}