Title: Defining and Discovering Hyper-meta-paths for Heterogeneous Hypergraphs
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Neural Networks

Hypergraph Neural Networks

Hypergraph Representation Learning
Abstract: Heterogeneous hypergraph is a kind of structural data that contains multiple types of nodes and multiple types of hyperedges. Each hyperedge type corresponds to a specific multi-ary relation (called hyper-relation) among subsets of nodes, which goes beyond traditional pair-wise relations in simple graphs. Existing representation learning methods for heterogeneous hypergraphs typically learn embeddings for nodes and hyperedges based on graph neural networks. Although achieving promising performance, they are still limited in capturing more complex structural features and richer semantics conveyed by the composition of various hyper-relations. To fill this research gap, in this work, we propose the concept of hyper-meta-path for heterogeneous hypergraphs, which is defined as the composition of a sequence of hyper-relations. Besides, we design an attention-based heterogeneous hypergraph neural network (HHNN) to automatically learn the importance of hyper-meta-paths. By exploiting useful ones, HHNN is able to capture more complex structural features to boost the model's performance, as well as leverage their conveyed semantics to improve the model's interpretability. Extensive experiments show that HHNN can achieve significantly better performance than state-of-the-art baselines, and the discovered hyper-meta-paths bring good interpretability for the model predictions. To facilitate the reproducibility of this work, we provide our dataset as well as anonymized source code at: https://github.com/zhengziyu77/HHNN.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study presents a novel concept of hyper-meta-path for heterogeneous hypergraphs where there are multiple types of nodes as well as multiple types of hyperedges. To be more specific, the hyper-meta-path is defined as the composition of a sequence of hyper-relations, and each hyper-relation describes a multi-ary relation, corresponding to one hyperedge type. The hyper-meta-path is expected to capture more complex structural features of heterogeneous hypergraphs. The authors design an attention-based heterogeneous hypergraph neural network to learn the importance of different hyper-meta-paths, and conduct extensive experiments to verify the effectiveness of the proposed concept.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The proposed concept of hyper-meta-path is novel and interesting, and is well defined. The authors have carefully analyzed its effectiveness, and verified its effectiveness through extensive experiments.\n\n- At the beginning of Section 1, the review of the existing four categories of graph data is well organized, and the four illustrations are intuitive.\n\n- The proposed model HHNN is clearly described by equations and intuitively described by Figure 3 and Figure 4. Therefore, the technical description of the methodology is highly readable and easy to follow.\n\n- The comparison experiments show that the performance of the proposed method is promising. The ablation study and hyper-meta-path discovery experiment show that the proposed methods can effectively discover useful hyper-meta-paths.\n\n- The authors have provided the source code and the used datasets, and provided much detailed information in the appendix, facilitating good reproducibility of this work.\n\n\n\n**Weaknesses**\n\n- The time complexity of the proposed method could be analyzed.\n\n- More baselines could be considered, and the related work could be further enriched.\n\n- According to the content, it is more appropriate to update the subtitle of Section 4.3 to \"Hyper-meta-path Discovery and Interpretability Study\".\n\n- It seems that there is a minor issue in Line 267. According to Figure 5(a), the {UMT} hyperedge and the {UMR} hyperedge are connected by a user node, instead of a movie node.\n\nQUESTIONS:\nMy questions are the same as the four weaknesses listed above. Please address those weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the important problem of capturing complex structural and semantic information in heterogeneous hypergraphs, which arises from the composition of different hyper-relations. To this end, the authors introduce a new concept, the \"hyper-meta-path,\" as a generalization of the well-known \"meta-path\" from heterogeneous simple graphs. Based on this concept, the authors propose a novel model, HHNN (Heterogeneous Hypergraph Neural Network). The core of HHNN is a three-level attention mechanism (node-to-hyperedge, intra-type hyperedge-to-node, and inter-type hyperedge-to-node). This architecture is designed to automatically learn the importance of different hyper-meta-paths without requiring manual specification, thereby aiming to improve both model performance and interpretability. The authors conduct experiments on two self-constructed datasets, showing that HHNN outperforms several baseline methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1. Clear and Valuable Problem Definition: The paper accurately identifies a key limitation in existing representation learning methods for heterogeneous hypergraphs: they often focus on individual hyper-relations while ignoring the richer, composite semantics that emerge from their combinations. The motivation to extend the \"meta-path\" concept to this domain is well-founded and promising.\n2.\tConceptual Contribution: The formal definition of the \"hyper-meta-path\" is a clear contribution of this work. It provides a new and useful theoretical lens for reasoning about and analyzing higher-order, heterogeneous dependencies within hypergraphs.\n\nWeaknesses\n\n1. Limited Methodological Novelty: The HHNN model's core architecture closely mirrors the HAN model's two-level attention mechanism (node-level and semantic-level). While HHNN adapts this for hypergraphs via β-Attention (intra-type) and γ-Attention (inter-type), it lacks fundamental innovation expected for top-tier venues like NeurIPS.  \n\n2. Weak Theoretical Grounding for Path Discovery: The claimed \"path discovery\" mechanism relies on attention weight products (Appendix C, Eq. 10), a heuristic approach common in GNNs but lacking rigorous theoretical justification. Without evidence linking high-weight paths to causal model behavior, the interpretability contribution is weakened.  \n\n3. Narrow Empirical Validation: Experiments are limited to two new datasets, raising concerns about potential bias. Benchmarking on standard hypergraph datasets (e.g., hypergraph Cora/PubMed) would better demonstrate generalizability and superiority over SOTA methods.  \n\n4. Lack of Computational Analysis: The three-level attention mechanism likely incurs high computational costs, yet the paper omits complexity analysis or runtime experiments. A brief mention of overhead (Appendix G) is insufficient, undermining the model's practical scalability.\n\nQUESTIONS:\n1. On the Scope and Rigor of Experimental Evaluation: I strongly advice to benchmark HHNN on widely used public datasets, even if they are derived from simple graphs (e.g., hypergraph versions of Cora, PubMed, DBLP, or a dataset from the TGB suite). \n\n2. On Scalability and Computational Complexity: A three-level attention mechanism is intuitively expensive. Add an experiment that plots the training time (or throughput) as the graph size (nodes/hyperedges) increases. This is a standard and necessary experiment to understand the practical limits of the proposed method.\n\n3. On the Definition of Hyper-meta-path: The current definition for composing hyper-relations (Rx ∩ Ry ≠ ∅) is very broad. It allows for composition as long as any node type is shared. Please discuss the potential limitations of this broad definition. For instance, if two hyper-relations only share a very generic and high-frequency node type (e.g., 'Timestamp' or 'Location'), their composition might be syntactically valid but semantically meaningless. Does your model implicitly learn to down-weight such compositions? A brief discussion of this point would add nuance to your work.\n\n4. Over-smoothing: A standard HHNN layer involves two steps of aggregation (node-to-hyperedge and hyperedge-to-node). Deeper GNN models often suffer from over-smoothing, where node representations become indistinguishable. How does your model, especially when stacked for multiple layers, mitigate this well-known issue?\n\nLIMITATIONS:\nYes. The authors have adequately addressed the limitations and potential societal impacts of their work. They have dedicated specific sections in the appendix (Appendix G for Limitations and Appendix H for Broader Impacts) to discuss these aspects.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the concept of hyper-meta-paths, a generalization of meta-paths to heterogeneous hypergraphs, and introduces HHNN, a GNN architecture designed to leverage hyper-meta-path semantics via multi-level attention. The authors claim that this framework captures richer relational structures and improves both performance and interpretability. Experimental results demonstrate improved performance over existing baselines across several datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nS1. The paper introduces the notion of hyper-meta-paths; a generalization of meta-paths for heterogeneous hypergraphs. This is appropriate for modeling multi-relational hypergraph semantics.\nS2. The proposed HHNN model uses a three-level attention aggregation scheme (node -> hyperedge -> node) to capture hyper-relational dependencies. The architecture is intuitive and well-structured.\nS3. HHNN outputs interpretable attention weights over hyper-meta-paths, which provides insight into which relational structures are most influential during prediction; an underexplored property in hypergraph learning.\n\nWeaknesses\nW1. The paper does not provide empirical evidence that hyper-meta-paths are essential. There is no comparison with meta-path-based or path-based baseline hypergraph models, making it unclear whether their modeling improves performance.\nW2. The number of hyper-meta-paths can grow rapidly, but the paper does not discuss how to limit or handle this. This may affect scalability on more complex graphs.\nW3. It is unclear whether all components of the model are necessary. The paper does not test what happens if parts of the attention mechanism are removed or modified.\nW4. The datasets used are relatively simple and may not be sufficient to demonstrate the benefit of modeling complex hyper-relational structures.\nW5. The model claims to offer interpretability, but no example or case study is provided to show that the learned hyper-meta-paths are meaningful.\n\nQUESTIONS:\nQ1. Can you compare HHNN with any path-based model to clarify the benefit of hyper-meta-paths?\nQ2. How do you deal with the rapid growth of hyper-meta-paths?\nQ3. Is the full three-level attention necessary? Any ablation results?\nQ4. Can you provide an example or visualization to support the interpretability claim?\nQ5. How does HHNN generalize to more complex heterogeneous hypergraphs with increased hyperedge arity and semantic diversity?\n\nLIMITATIONS:\nThe proposed method presents scalability challenges due to the rapid growth of hyper-meta-paths in heterogeneous hypergraphs. The claim of interpretability is not supported by concrete examples or analysis. Furthermore, the evaluation is conducted on relatively simple datasets, limiting insight into the broader applicability of the proposed approach.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose Heterogeneous Hypergraph Neural Network (HHNN) to effectively capture complex structural features and richer semantics in heterogeneous hypergraphs by introducing the novel concept of hyper-meta-paths.\n\nAuthors define hyper-meta-paths as sequences of hyper-relations that share common node types, generalizing the meta-path concept from simple graphs to heterogeneous hypergraphs to encode multi-ary relations. To leverage these, authors design HHNN, a graph neural network architecture employing three levels of attention mechanisms: α-attention aggregates node features into hyperedge representations, β-attention aggregates hyperedge features intra-type back to nodes, and γ-attention aggregates across hyperedge types. This design allows HHNN to automatically discover and exploit important hyper-meta-paths, enhancing both performance and interpretability.\n\nAuthors perform extensive experiments on two newly constructed real-world heterogeneous hypergraph datasets (Movielens and Olist), demonstrating that HHNN significantly outperforms state-of-the-art baselines in node classification tasks. Additionally, ablation studies verify the critical role of each attention mechanism in HHNN, and case analyses show that the discovered hyper-meta-paths provide meaningful semantic interpretations for model predictions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- Both heterogeneous hypergraphs and the novel concept of hyper-meta-paths are clearly defined, and are well incorporated into existing graph neural network concepts with 3 attention layers.\n- Authors perform qualitative analyses and ablation studies, which not only help readers understand how the model works but also validate the specific contributions of the multi-level attention mechanisms, lending credibility and interpretability to the proposed method. In particular, the case study clearly shows the role of each attention layer.\n- HHNN achieves consistently superior performance over given real-world datasets. Along with the ablation study, the experimental results demonstrate the practical benefits of introducing hyper-meta-paths and the effectiveness of the attention mechanisms.\n\nWeaknesses\n- As with many graph-based methods, how hyperedges and hyper-meta-paths are defined from raw data significantly influences model performance. However, the paper does not provide guidance, heuristics, or sensitivity analyses for constructing these structures, limiting practical applicability in diverse domains.\n- The authors evaluate HHNN on only two datasets (Movielens and Olist), both drawn from recommendation and e-commerce domains. This narrow empirical scope raises concerns about how well the method generalizes to other domains such as biomedical networks, financial graphs, or knowledge graphs.\n- The high-level idea behind HHNN—modeling multi-entity relationships as hyperedges and learning over their compositions—is conceptually very similar to Relational Deep Learning (RDL), where database tuples are modeled as nodes and joins as edges to enable graph representation learning directly on relational databases. However, the paper does not discuss or compare itself to this relevant body of work, notably missing connections to: Fey, M., et. al., Relational Deep Learning: Graph Representation Learning on Relational Databases. In Proceedings of the 41st International Conference on Machine Learning, ICML 2024.\n\nQUESTIONS:\n- First of all, the proposed approach does not seem very different from apply graph neural networks over the graphs where all the relations are represented as nodes. How can authors compare the HNNN with such concepts? This comparison needs to be addressed and studied.\n- In Figure 3, m_{3}' needs to be m_{5}'.\n- Like Figure 3, 3 different attention layers are proposed. However, one of the most straightforward ways is to use a giant attention over all the neighbor nodes connected through hyperedges. That is, instead of \\alpha, \\beta, and \\gamma, one can think of only one attention over all the neighbors or 2 attentions (1 attention over different types and the other attention for each type's neighbors). What is the rationale of choosing the current architecture?\n- Please include minimal explanation about the experimental datasets, so that at least readers can understand what are hperedges and what each UMT means.\n- Similarly, please explain what 20% and 40% means in Table 1 and 2.\n- Why do authors use different settings for Table 3 from Table 1 and 2? This reduces the interpretability of each ablation case compared to the other methods.\n- Related work is recommended to be included in the main content.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-04"}