Search Results (23)

Complex real-world systems are often modeled as heterogeneous information networks with diverse node and relation types, bringing new opportunities and challenges to link prediction. Traditional methods based on similarity or meta-paths fail to fully capture high-order structures and semantics, while existing hypergraph-based models homogenize all high-order information without considering their importance differences, diluting core associations with redundant noise and limiting prediction accuracy. Given these issues, we propose the VE-HGCN, a link prediction model for HINs that fuses hypergraph convolution with soft-voting ensemble strategy. The model first constructs multiple heterogeneous hypergraphs from HINs via network frequent subgraph pattern extraction, then leverages hypergraph convolution for node representation learning, and finally employs a soft-voting ensemble strategy to fuse multi-model prediction results. Extensive experiments on four public HIN datasets show that the VE-HGCN outperforms seven mainstream baseline models, thereby validating the effectiveness of the proposed method. This study offers a new perspective for link prediction in HINs and exhibits good generality and practicality, providing a feasible reference for addressing high-order information utilization issues in complex heterogeneous network analysis. Full article

Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs) have achieved significant success, they often overlook the diversity and dynamics of user preferences. To address these issues, researchers have begun to employ Hypergraph Convolutional Networks (HGCNs) for disentangled representation learning. However, two critical problems have received less attention: (1) the limited expressive capacity of conventional hypergraph convolution layers, which restricts the modeling of complex nonlinear user–POI preference interactions and consequently weakens generalization performance, and (2) the inadequate utilization of contrastive learning mechanisms, which prevents fully capturing cross-view collaborative signals and limits the exploitation of complementary multi-view information. To tackle these challenges, we propose a Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning (NE-DCHL) for next POI recommendation. The proposed model enhances nonlinear modeling capability and generalization by integrating ReLU activation, residual connections, and dropout regularization within the hypergraph convolution layer. A K-Nearest Neighbor (KNN)-based weighted adjacency matrix is employed to construct the geographical-view hypergraph, reducing computational complexity while maintaining essential spatial correlations. Moreover, a mini-batch InfoNCE loss and the GRACE (deep GRAph Contrastive rEpresentation learning) framework are utilized to improve efficiency and cross-view collaboration. Extensive experiments on two real-world datasets demonstrate that NE-DCHL consistently outperforms the original DCHL and other state-of-the-art approaches. Full article

Prostate cancer is a global health concern, and early diagnosis plays a vital role in improving the survival rate. Accurate segmentation is a key step in the automated diagnosis of prostate cancer; however, manual segmentation remains time-consuming and challenging. Micro-Ultrasound (US) is particularly well-suited for prostate cancer detection, offering real-time imaging with a resolution comparable to that of MRI. This enables improved spatial resolution and detailed visualization of small anatomical structures. With recent advances in deep learning for medical image segmentation, precise prostate segmentation has become critical for biopsy guidance, disease diagnosis, and follow-up. However, segmentation of the prostate in micro-US images remains challenging due to indistinct boundaries between the prostate and surrounding tissue. In this work, we propose a model for precise micro-ultrasound image segmentation. The model employs a dual-encoder architecture that integrates Convolutional Neural Networks (CNN) and Transformer-based encoders in parallel, combined with a fusion module to capture both global dependencies and low-level spatial details. More importantly, we introduce a decoder based on Mamba v2 to enhance segmentation accuracy. A Hypergraph Neural Network (HGNN) is employed as a bridge between the dual encoders and Mamba decoder to model correlations among non-pairwise connections. Experimental results on micro-US datasets demonstrated that our model achieved superior or comparable performance to state-of-the-art methods, with a Dice score of 0.9416 and an HD95 of 1.93. Full article

Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we propose a novel dynamic hypergraph-based risk model (DyHRM) which integrates the encoder–decoder (ED) architecture with hypergraph convolutional neural networks (HGCNs). The risk model is used to generate longitudinal forecasts of KOA incidence and progression based on the knee evolution at a historical stage. DyHRM comprises two main parts, namely the dynamic hypergraph gated recurrent unit (DyHGRU) and the multi-view HGCN (MHGCN) networks. The ED-based DyHGRU follows the sequence-to-sequence learning approach. The encoder first transforms a knee sequence at the historical stage into a sequence of hidden states in a latent space. The Attention-based Context Transformer (ACT) is designed to identify important temporal trends in the encoder’s state sequence, while the decoder is used to generate sequences of KOA progression, at the prediction stage. MHGCN conducts multi-view spatial HGCN convolutions of the original knee data at each step of the historic stage. The aim is to acquire more comprehensive feature representations of nodes by exploiting different hyperedges (views), including the global shape descriptors of the cartilage volume, the injury history, and the demographic risk factors. In addition to DyHRM, we also propose the HyGraphSMOTE method to confront the inherent class imbalance problem in KOA datasets, between the knee progressors (minority) and non-progressors (majority). Embedded in MHGCN, the HyGraphSMOTE algorithm tackles data balancing in a systematic way, by generating new synthetic node sequences of the minority class via interpolation. Extensive experiments are conducted using the Osteoarthritis Initiative (OAI) cohort to validate the accuracy of longitudinal predictions acquired by DyHRM under different definition criteria of KOA incidence and progression. The basic finding of the experiments is that the larger the historic depth, the higher the accuracy of the obtained forecasts ahead. Comparative results demonstrate the efficacy of DyHRM against other state-of-the-art methods in this field. Full article

Mitotic figures in tumor tissues are an important criterion for diagnosing malignant lesions, and physicians often search for the presence of mitosis in whole slide imaging (WSI). However, prolonged visual inspection by doctors may increase the likelihood of human error. With the advancement of deep learning, AI-based automatic cytopathological diagnosis has been increasingly applied in clinical settings. Nevertheless, existing diagnostic models often suffer from high computational costs and suboptimal detection accuracy. More importantly, when assessing cellular abnormalities, doctors frequently compare target cells with their surrounding cells—an aspect that current models fail to capture due to their lack of intercellular information modeling, leading to the loss of critical medical insights. To address these limitations, we conducted an in-depth analysis of existing models and propose an Inter–Intra Hypergraph Neural Network (II-HGNN). Our model introduces a block-based feature extraction mechanism to efficiently capture deep representations. Additionally, we leverage hypergraph convolutional networks to process both intracellular and intercellular information, leading to more precise diagnostic outcomes. We evaluate our model on publicly available datasets under varying imaging conditions, and experimental results demonstrate that our approach consistently outperforms baseline models in terms of accuracy. Full article

Accurate fault diagnosis in aerospace transmission systems is essential for ensuring equipment reliability and operational safety, especially for aero-engine bearings. However, current approaches relying on Convolutional Neural Networks (CNNs) for Euclidean data and Graph Convolutional Networks (GCNs) for non-Euclidean structures struggle to simultaneously capture heterogeneous data properties and complex spatio-temporal dependencies. To address these limitations, we propose a novel Spatial–Temporal Hypergraph Fault Diagnosis framework (STHFD). Unlike conventional graphs that model pairwise relations, STHFD employs hypergraphs to represent high-order spatial–temporal correlations more effectively. Specifically, it constructs distinct spatial and temporal hyperedges to capture multi-scale relationships among fault signals. A type-aware hypergraph learning strategy is then applied to encode these correlations into discriminative embeddings. Extensive experiments on aerospace fault datasets demonstrate that STHFD achieves superior classification performance compared to state-of-the-art diagnostic models, highlighting its potential for enhancing intelligent fault detection in complex aerospace systems. Full article

Conventional remote sensing classification approaches based on single-source data exhibit inherent limitations, driving significant research interest in improved multimodal data fusion techniques. Although deep learning methods based on convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs) have demonstrated promising results in fusing complementary multi-source data, existing methodologies demonstrate limited efficacy in capturing the intricate higher-order spatial–spectral dependencies among pixels. To overcome these limitations, we propose HGCN-HL, a novel multimodal deep learning framework that integrates hypergraph convolutional networks (HGCNs) with lightweight CNNs. Specifically, an adaptive weight mechanism is first designed to preliminarily fuse the spectral features of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR), enhancing the feature representation ability. Then, superpixel-based dynamic hyperedge construction enables the joint characterization of homogeneous regions across both modalities, significantly boosting large-scale object recognition accuracy. Finally, local detail features are captured through a parallel CNN branch, complementing the global relationship modeling of the HGCN. Comprehensive experiments conducted on three benchmark datasets demonstrate the superior performance of our method compared to existing state-of-the-art approaches. Notably, the proposed framework achieves significant improvements in both training efficiency and inference speed while maintaining competitive accuracy. Full article

Knee osteoarthritis (KOA) is a highly prevalent muscoloskeletal joint disorder affecting a significant portion of the population worldwide. Accurate predictions of KOA progression can assist clinicians in drawing preventive strategies for patients. In this paper, we present an integrated approach based on hypergraph convolutional networks (HGCNs) for longitudinal predictions of KOA grades and progressions from MRI images. We propose two novel models, namely, the C_Shape.Net and the predictor network. The C_Shape.Net operates on a hypergraph of volumetric nodes, especially designed to represent the surface and volumetric features of the cartilage. It encompasses deep HGCN convolutions, graph pooling, and readout operations in a hierarchy of layers, providing, at the output, expressive 3D shape descriptors of the cartilage volume. The predictor is a spatio-temporal HGCN network (ST_HGCN), following the sequence-to-sequence learning scheme. Concretely, it transforms sequences of knee representations at the historical stage into sequences of KOA predictions at the prediction stage. The predictor includes spatial HGCN convolutions, attention-based temporal fusion of feature embeddings at multiple layers, and a transformer module that generates longitudinal predictions at follow-up times. We present comprehensive experiments on the Osteoarthritis Initiative (OAI) cohort to evaluate the performance of our methodology for various tasks, including node classification, longitudinal KL grading, and progression. The basic finding of the experiments is that the larger the depth of the historical stage, the higher the accuracy of the obtained predictions in all tasks. For the maximum historic depth of four years, our method yielded an average balanced accuracy (BA) of 85.94% in KOA grading, and accuracies of 91.89% (+1), 88.11% (+2), 84.35% (+3), and 79.41% (+4) for the four consecutive follow-up visits. Under the same setting, we also achieved an average value of Area Under Curve (AUC) of 0.94 for the prediction of progression incidence, and follow-up AUC values of 0.81 (+1), 0.77 (+2), 0.73 (+3), and 0.68 (+4), respectively. Full article

Session-based recommendation (SBR) aims to predict a user’s next interests based on their actions in a single visit. Recent methods utilize graph neural networks to study the pairwise relationship of item transfers, yet these often overlook the complex high-order connections between items. Hypergraphs can naturally model many-to-many relationships and capture complex interactions, thereby improving the accuracy of SBR. However, the potential of hypergraphs in SBR remains underexplored. This paper models session data into two views: the hypergraph view, which employs hypergraph convolution, and the session view, which utilizes the intersection graph of the hypergraph with standard graph convolution to support the main recommendation task. By combining cross-view contrastive learning with view adversarial training as an auxiliary task, the two views recursively exploit different connectivity information to generate ground truth samples, thus enriching the session information. Extensive experiments on three benchmark datasets confirm the effectiveness of our hypergraph modeling approach and cross-view contrastive learning. Full article

The resource scheduling optimization problem in predictive maintenance is a complex operational research challenge involving reasoning about stochastic failure models and the dynamic allocation of repair resources. In recent years, resource scheduling methods based on deep learning have been increasingly applied in this field, demonstrating promising performances. Among these, resource scheduling algorithms based on heterogeneous graphs have shown exceptional results in multi-objective optimization tasks. However, conventional graph neural networks primarily operate on binary relational graphs, which struggle to effectively utilize data in multi-relational settings, thereby limiting the scheduler’s performance. To address this limitation, this paper proposes a heterogeneous hypergraph-based resource scheduling algorithm for aircraft maintenance tasks to tackle the challenges of higher-order and many-to-many relationship processing inherent in traditional graph neural networks. Specifically, the proposed algorithm defines aircraft nodes and maintenance personnel nodes while introducing decision nodes and state nodes to construct hyperedges. It employs hypergraph convolution with a multi-head attention mechanism to learn the long-term value of decisions, followed by policy selection based on a Markov decision process. This method offers a lightweight, non-parametric dynamic scheduling solution capable of robust learning in highly stochastic environments. Comparative experiments conducted on three datasets of varying scales demonstrate that the proposed method outperforms both heuristic algorithms and existing deep learning methods in terms of its optimization performance on M1 and M2 metrics. Furthermore, it surpasses resource scheduling algorithms based on heterogeneous graph neural networks across multiple metrics. Full article

Many important plants’ agronomic traits, such as crop yield, stress tolerance, and other traits, are controlled by multiple genes and exhibit complex inheritance patterns. Traditional breeding methods often encounter difficulties in dealing with these traits due to their complexity. However, genomic selection (GS), which utilizes high-density molecular markers across the entire genome to facilitate selection in breeding programs, excels in capturing the genetic variation associated with these traits. This enables more accurate and efficient selection in breeding. The traditional crop genome selection model, based on statistical methods or machine learning models, often treats samples as independent entities while neglecting the abundance latent relational information among them. Consequently, this limitation hampers their predictive performance. In this study, we proposed a novel crop genome selection model based on hypergraph attention networks for genomic prediction (HGATGS). This model incorporates dynamic hyperedges that are designed based on sample similarity to validate the efficacy of high-order relationships between samples for phenotypic prediction. By introducing an attention mechanism, it assigns weights to different hyperedges and nodes, thereby enhancing the ability to capture kinship relationships among samples. Additionally, residual connections are incorporated between hypergraph convolutional layers to further improve model stability and performance. The model was validated on datasets for multiple crops, including wheat, corn, and rice. The results showed that HGATGS significantly outperformed traditional statistical methods and machine learning models on the Wheat 599, Rice 299, and G2F 2017 datasets. On Wheat 599, HGATGS achieved a correlation coefficient of 0.54, a 14.9% improvement over methods like R-BLUP and BayesA (0.47). On Rice 299, HGATGS reached 0.45, a 66.7% increase compared to other models like R-BLUP and SVR (0.27). On G2F 2017, HGATGS attained 0.88, slightly surpassing other models like R-BLUP and BayesA (0.87). We conducted ablation experiments to compare the model’s performance across three datasets, and found that the model integrating hypergraph attention and residual connections performed optimally. Subsequent comparisons of the model’s prediction performance with dynamically selected different k values revealed optimal performance when K = (3,4). The model’s prediction performance was also compared across different single nucleotide polymorphisms (SNPs) and sample sizes in various datasets, with HGATGS consistently outperforming the comparison models. Finally, visualizations of the constructed hypergraph structures showed that certain nodes have high connection densities with hyperedges. These nodes often represent varieties or genotypes with significant impacts on traits. During feature aggregation, these high-connectivity nodes contribute significantly to the prediction results and demonstrate better prediction performance across multiple traits in multiple crops. This demonstrates that the method of constructing hypergraphs through correlation relationships for prediction is highly effective. Full article

In recent years, it has become a hot topic to combine graph neural networks with contrastive learning, a method which has been applied not only in recommendation tasks but has also achieved impressive results in many fields, such as text processing and spatio-temporal modeling. However, existing methods are still constrained by several issues: (1) Most graph learning methods do not explore the imbalance of node and edge type distribution caused by different user–item interactions. (2) The randomness of data expansion and sampling strategies in contrastive learning may lead to confusion about the importance of key items for users. To overcome the problems, in this paper, we propose an explanation-guided contrastive recommendation model based on interactive message propagation and dual-hypergraph convolution (ECR-ID). Specifically, we designed two different interactive propagation mechanisms for the user–item dual-hypergraph sets to promote comprehensive dynamic information propagation and exchange, which further mitigates the imbalance problem of hyperedges and nodes in the hypergraph convolution, as well as the propagation loss of synergistic information between nodes. In addition, we developed an explanation-guided contrastive learning framework, which highlights the important items in user–item interactions through an explanation-based approach and guided the training of the contrastive learning framework based on the differences in the importance scores of the items, thus generating accurate positive and negative views and improving the contrastive learning performance. Finally, we integrated the contrastive learning framework with the dual-hypergraph networks based on joint training to further improve the recommendation performance. Extensive experimental evaluations on real datasets show that ECR-ID outperforms state-of-the-art recommendation algorithms. In the future, we will conduct in-depth tests based on a wider range of real-world datasets to alleviate the limitation that the existing experimental datasets all comprise data from single business services like Alibaba and Amazon, thus validating the effectiveness of our model more comprehensively. Full article

Deep learning-based approaches for automatic depression recognition offer advantages of low cost and high efficiency. However, depression symptoms are challenging to detect and vary significantly between individuals. Traditional deep learning methods often struggle to capture and model these nuanced features effectively, leading to lower recognition accuracy. This paper introduces a novel multimodal depression recognition method, HYNMDR, which utilizes hypergraphs to represent the complex, high-order relationships among patients with depression. HYNMDR comprises two primary components: a temporal embedding module and a hypergraph classification module. The temporal embedding module employs a temporal convolutional network and a negative sampling loss function based on Euclidean distance to extract feature embeddings from unimodal and cross-modal long-time series data. To capture the unique ways in which depression may manifest in certain feature elements, the hypergraph classification module introduces a threshold segmentation-based hyperedge construction method. This method is the first attempt to apply hypergraph neural networks to multimodal depression recognition. Experimental evaluations on the DAIC-WOZ and E-DAIC datasets demonstrate that HYNMDR outperforms existing methods in automatic depression monitoring, achieving an F1 score of 91.1% and an accuracy of 94.0%. Full article

For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security. Full article

Graph convolutional networks (GCN) are an important research method for intelligent transportation systems (ITS), but they also face the challenge of how to describe the complex spatio-temporal relationships between traffic objects (nodes) more effectively. Although most predictive models are designed based on graph convolutional structures and have achieved effective results, they have certain limitations in describing the high-order relationships between real data. The emergence of hypergraphs breaks this limitation. A dynamic spatio-temporal hypergraph convolutional network (DSTHGCN) model is proposed in this paper. It models the dynamic characteristics of traffic flow graph nodes and the hyperedge features of hypergraphs simultaneously, achieving collaborative convolution between graph convolution and hypergraph convolution (HGCN). On this basis, a hyperedge outlier removal mechanism (HOR) is introduced during the process of node information propagation to hyper-edges, effectively removing outliers and optimizing the hypergraph structure while reducing complexity. Through in-depth experimental analysis on real-world datasets, this method has better performance compared to other methods. Full article