Aug 2026· IEEE Transactions on Pattern Analysis and Machine Intelligence· Vol PP, pp. 1-17· 1 citation
Medicine
TL;DR
The Hypergraph Identity-Aware Subtree (IA Subtree) Kernel is introduced, which distinguishes uniform-regular hypergraphs by considering both neighborhood connectivity and connection density and develops two Hypergraph Neural Networks: Hypergraph Isomorphism Networks (HGIN) and Identity-Aware Hypergraph Isomorphism Networks (IA-HGIN).
Abstract
Isomorphism recognition is crucial for analyzing complex network structures. Traditional methods like Weisfeiler-Lehman (WL) kernels and various GNNs often overlook higher-order interactions essential for practical applications. Besides, hypergraph WL kernels struggle to distinguish uniform-regular hypergraphs due to their focus on neighborhood connectivity without effectively capturing unique higher-order structures. To overcome these issues, we introduce the Hypergraph Identity-Aware Subtree (IA Subtree) Kernel, which distinguishes uniform-regular hypergraphs by considering both neighborhood connectivity and connection density. This kernel detects subtle differences in hypergraph structures via variations in Closed Paths of different lengths. Additionally, we develop two Hypergraph Neural Networks: Hypergraph Isomorphism Networks (HGIN) and Identity-Aware Hypergraph Isomorphism Networks (IA-HGIN). These models combine the strengths of the Hypergraph WL subtree kernel with advanced neural architectures, improving classification by integrating features from closed-path distributions. We also provide the first comprehensive theoretical comparison of expressive power between kernel-based methods and neural networks, confirming IA-HGIN's superior performance. Experimental results on eight synthetic and eight real hypergraph datasets validate the effectiveness of our methods over existing State-of-the-Art approaches.
Experiments show that replacing PageRank with alternative centralities yields similar F1-scores while offering notable runtime savings, and that GraphHD-Order remains competitive with the original GraphHD baseline while providing consistent speedups in encoding time.
A novel directed hypergraph motif-based neural network (DHMNN) for directed hyperlink prediction, which simultaneously captures higher order structural and connectivity information from the directed hypergraph topology and significantly outperforms state-of-the-art models.
Xihang Meng, Hao Peng, Guangjie Zeng et al.· IEEE Transactions on Neural...· 0 citations
DIAL, a message-passing layer that gives nodes access to graph structure through the diagonal of graph-derived operators, is introduced, which uses randomized probing to provide nodes with learnable, permutation equivariant access to diagonal entries.
Saku Peltonen, H. Bilgi, Kubilay Atasu· 0 citations
A Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of-Distribution detection (termed HGOOD), which constructs two branches to hierarchically mine graph compact semantics in a comprehensive manner and introduces a cross-branch prototype contrast that aligns the captured graph patterns with their cross-branch clustering prototypes to enhance the semantic manifold of the in-distribution graph.
Xuan-Ting Fan, Chenyu Wang, Yue-Yue Gao et al.· 0 citations
Recommendation plays a crucial role in the modern Web ecosystem, powering personalized services across e-commerce, social platforms, and online content networks. To model complex user–item interactions in such Web environments, Graph Neural Networks (GNNs) have become a popular and effective approach due to their ability to capture relational dependencies. However, existing GNN-based methods face some challenges, such as limited capacity for nonlinear representation, inability to capture global structural information, and susceptibility to noise in user interaction data. Although self-supervised learning methods have been introduced to address these issues, these methods often overlook the intricate dependencies between users and items and fail to effectively utilize high-order global information. To address these challenges, we propose Fourier Kolmogorov-Arnold Network and Hypergraph Enhanced Contrastive Learning (FHCL) for recommendation. Our method constructs two complementary views: a graph generative view and a denoising view. In the graph generative view, we use the Fourier Kolmogorov-Arnold Network (Fourier KAN) to enhance the nonlinear representation capabilities by decomposing complex user-item interactions. Subsequently, we employ Variational Graph Auto-Encoders (VGAE) to reconstruct the graph structure, extracting meaningful structural information while mitigating the impact of noise. Then we use hypergraph learning to capture high-order global dependencies. In the denoising view, we introduce a denoising matrix to filter noisy edges and further combine hypergraph learning to improve user preferences. Finally, we integrate these views through contrastive learning to generate robust and accurate recommendations. Extensive experiments on two public datasets demonstrate the superior performance of FHCL, while comprehensive ablation studies validate the necessity and effectiveness of each component.
Yuwen Liu, Lianyong Qi, Xucheng Zhou et al.· Annual International ACM SIG...· 0 citations
Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on labeled data, making them vulnerable to label noise. Despite advances in learning with label noise (LLN) and graph learning with label noise (GLN), noisy-label learning on hypergraphs remains underexplored. In this paper, we present a systematic study of hypergraph node classification under label noise. First, we adapt representative LLN and GLN methods to hypergraphs and evaluate them under a unified benchmark, revealing the limitations of existing robust learning strategies for hypergraphs. Building on this, we propose a new hypergraph robust framework, HyperTrust, which first estimates hyperedge trustworthiness through a pretraining-based, entropy-aware strategy, and then incorporates the HyperedgeBoost module to enhance reliable supervision by connecting unlabeled nodes to trustworthy hyperedges, as well as the HyperedgePrune module to suppress noisy propagation by removing untrustworthy node-hyperedge incidences. Finally, two modules work collaboratively to adjust the hypergraph structure and generate final predictions. Extensive experiments and theoretical analysis demonstrate the effectiveness and robustness of HyperTrust on multiple hypergraph datasets under various noisy settings. Our work provides a unified benchmark and an effective solution for hypergraph learning with label noise and lays a foundation for future research in this direction.
Mengyao Zhou, Zhiheng Zhou, Xiao Han et al.· 0 citations
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