Graph Neural Networks (GNNs) demonstrate promising performance in data mining yet exhibit inherent vulnerabilities to adversarial attacks. Even imperceptible perturbations degrade model performance, seriously hindering the application of GNNs in reality. In recent years, adversarial defense methods based on model architecture have gained attention for their effectiveness. However, they exhibit limited effectiveness against emerging black-box influence maximization attacks (IMAs), which aim to maximize the spread of feature perturbations through a group of influential nodes. This may leave a potential risk in real-world applications. To address this issue, we propose a Graph Adversarial Defense method based on the Hilbert-Schmidt Independence Criterion (HSIC-GAD). Specifically, the proposed method leverages hidden representations to capture the dependence between preprocessed node features and label information. On this basis, we design a regularizer that simultaneously preserves the most relevant information for downstream tasks while filtering out adversarial perturbations from the input data. A simple theoretical analysis shows that the HSIC regularizer can reduce the sensitivity of the model to adversarial inputs. Additionally, it exhibits strong universality, consistently enhancing the adversarial robustness of diverse models. Extensive experiments on real-world datasets demonstrate that HSIC-GAD outperforms state-of-the-art defense methods against IMAs.

Semi-supervised graph anomaly detection confronts the fundamental challenge of identifying anomalous nodes that exhibit deviations from normal graph patterns in node attributes or structure connectivity, using a small set of labeled normal nodes. Existing methods fall short in modeling the spectrum of anomaly severity, as they generate anomalies in a single, undifferentiated step. This oversight restricts the detection of complex anomalies. In this paper, we propose a progressive anomaly generation network (PAGE) to overcome this limitation. PAGE enhances complex anomaly detection through progressive attribute-structure perturbation: It first injects attribute noise into selected normal nodes to generate pseudo-anomalous nodes. Then it applies progressive structure perturbation to create hybrid anomalies that simulate the evolution from mild to complex perturbations. Importantly, the proposed anomaly progressive constraint loss enforces that hybrid anomalies exhibit a higher degree of abnormality than primary anomalies, enhancing the model's anomaly quantification capability. PAGE further integrates deviation constraint loss and classification loss to optimize node representations and improve prediction. Extensive experiments on benchmark datasets demonstrate that PAGE significantly outperforms state-of-the-art methods, demonstrating its effectiveness for graph anomaly detection.

Cold-start sequential recommendation, where user interaction histories are sparse or minimal, remains a significant challenge in recommendation systems. Current meta-learning-based approaches rely heavily on the interaction histories of regular users to construct meta-tasks, aiming to acquire prior knowledge for cold-start adaptation. However, these methods often fail to account for preference discrepancies between regular and cold-start users, leading to biased preference modeling and suboptimal recommendations. To address this issue, we propose a novel counterfactual task-augmented meta-learning method for cold-start sequential recommendations. Our approach intervenes in user interaction histories to create counterfactual sequences that simulate potential but unrealized user behaviors, establishing counterfactual tasks within a meta-learning framework. Additionally, we aggregate meta-path neighbors to uncover latent relationships between items, enabling more detailed and accurate modeling of user preferences. Moreover, by integrating real and counterfactual task losses, we jointly optimize the model through a combination of global and local updates, enhancing its adaptability to cold-start scenarios. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art techniques, achieving superior results in cold-start sequential recommendation tasks.

Convolutional neural networks (CNNs) excel at learning spatial patterns through local receptive fields, while channel attention mechanisms enhance feature discrimination by emphasizing informative channels. Traditional approaches like SENet generate channel weights through global pooling and bottlenecked MLPs, but fundamentally ignore cross-channel dependencies—a critical limitation that discards contextual relationships between features. To address this, we propose the Cross-Channel Graph Attention mechanism (CCA), which explicitly models channel interactions via graph neural networks. Specifically, CCA represents channels as graph nodes, constructs sparse KNN graphs to capture inter-channel dependencies, and performs attention-based feature aggregation. Theoretically, we prove that CCA strictly expands the expressive power of conventional channel attention through full-rank attention matrices, while maintaining superior generalization via sparsity-induced Rademacher complexity bounds. Furthermore, we establish that CCA can universally approximate any continuous channel mixing function with controlled neighbor size (Theorem 3). Extensive experiments on image classification (CIFAR-10/100, mini-ImageNet) and super-resolution (Set5/14) validate that CCA-equipped networks achieve state-of-the-art performance—improving ResNet34 by 2.16% TOP-1 accuracy on CIFAR-10 and boosting VDSR’s PSNR by 0.43dB on ×3 scaling. Both theoretical and empirical analyses demonstrate that CCA achieves an optimal balance between feature interaction capacity and computational efficiency through its graph-structured attention paradigm. The code is available at https://github.com/openreview-pro/CCA

Multi-modal point cloud completion, which utilizes a complete image and a partial point cloud as input, is a crucial task in 3D computer vision. Previous methods commonly employ a cross-attention mechanism to fuse point clouds and images. However, these approaches often fail to fully leverage image information and overlook the intrinsic geometric details of point clouds that could complement the image modality. To address these challenges, we propose an interleaved attention enhanced Transformer (IAET) with three main components, i.e., token embedding, bidirectional token supplement, and coarse-to-fine decoding. IAET incorporates a novel interleaved attention mechanism to enable bidirectional information supplementation between the point cloud and image modalities. Additionally, to maximize the use of the supplemented image information, we introduce a view-guided upsampling module that leverages image tokens as queries to guide the generation of detailed point cloud structures. Extensive experiments demonstrate the effectiveness of IAET, highlighting its state-of-the-art performance on multi-modal point cloud completion benchmarks in various scenarios. The source code is freely accessible at https://github.com/doldolOuO/IAET.

Hypergraphs offer a natural paradigm for modeling complex systems with multi-way interactions. Hypergraph neural networks (HGNNs) have demonstrated remarkable success in learning from such higher-order relational data. While such higher-order modeling enhances relational reasoning, the effectiveness of hypergraph learning remains bottlenecked by two persistent challenges: the scarcity of labeled data inherent to complex systems, and the vulnerability to structural noise in real-world interaction patterns. Traditional data augmentation methods, though successful in Euclidean and graph-structured domains, struggle to preserve the intricate balance between node features and hyperedge semantics, often disrupting the very group-wise interactions that define hypergraph value. To bridge this gap, we present HyperMixup, a hypergraph-aware augmentation framework that preserves higher-order interaction patterns through structure-guided feature mixing. Specifically, HyperMixup contains three critical components: 1) Structure-aware node pairing guided by joint feature-hyperedge similarity metrics, 2) Context-enhanced hierarchical mixing that preserves hyperedge semantics through dual-level feature fusion, and 3) Adaptive topology reconstruction mechanisms that maintain hypergraph consistency while enabling controlled diversity expansion. Theoretically, we establish that our method induces hypergraph-specific regularization effects through gradient alignment with hyperedge covariance structures, while providing robustness guarantees against combined node-hyperedge perturbations. Comprehensive experiments across diverse hypergraph learning tasks demonstrate consistent performance improvements over state-of-the-art baselines, with particular effectiveness in low-label regimes. The proposed framework advances hypergraph representation learning by unifying data augmentation with higher-order topological constraints, offering both practical utility and theoretical insights for relational machine learning.

Multi-criteria (MC) ratings as auxiliary supervisory signals can improve the prediction accuracy of recommender systems. The existing MC methods learn the representations of users and items in Euclidean space to estimate the interaction probabilities. However, this modeling paradigm ignores two important aspects. Firstly, when embedding power-law distribution data and personalized MC preferences in Euclidean space, the model may produce suboptimal solutions due to the distortion of the hierarchical structure. Secondly, the inevitable noise in MC ratings may hinder the recommendation quality of the model. To address the above issues, we propose a novel framework called Hyperbolic Multi-Criteria Recommendation (HMCR), which aims to mine users' MC behavioral features on hyperbolic manifolds and mitigate the noise interference through knowledge transfer among the criteria. Specifically, we map the representations on each criterion view to a hyperbolic space with adjustable curvature based on the Lorentz model, which is used to capture the hierarchical structure of collective user behavior. The MC preferences of individual users are fused by calculating the hyperbolic attention among each criterion and the overall rating. Moreover, we design a self-supervised contrastive loss to suppress the negative impact of noise interactions on the model. The experimental results on four real-world datasets show that the HMCR significantly outperforms the existing baselines.

Dynamic graph representation learning aims to capture the evolution of graph structures and obtain accurate node embeddings, a crucial task in graph machine learning. The Hawkes point process, a mathematical framework effective for modeling the influence of historical events on future occurrences, has been validated as a powerful tool for capturing the dynamics of graph evolution in dynamic graph representation learning. However, existing dynamic graph representation learning methods based on the Hawkes point process primarily model excitation at the individual node level, failing to adequately account for structural influences during graph evolution. This limitation restricts their ability to comprehensively capture network evolution patterns. To address this limitation, we propose a Hawkes Point Process-enhanced Dynamic Graph Neural Network (HP-DGNN) model. This model leverages the Hawkes point process to model both individual node histories and structural histories, capturing their respective influences on future node interactions. By integrating individual and structural influences in computing Hawkes conditional intensity, the model comprehensively captures the impacts of both layers on future node interactions. We evaluate our proposed model on two downstream tasks of dynamic graph representation learning: dynamic link prediction and future node degree prediction. Compared to 12 state-of-the-art methods, our model consistently demonstrates superior performance, underscoring its effectiveness in capturing the complexities of graph evolution.

Recommender systems are designed to assist users in discovering interesting items and bringing profits to online platforms. The existing works primarily explore the correlation between historical feedback and model predictions through the data-driven paradigm based on a single user-item rating matrix (i.e., overall rating). However, this single-criterion methods ignore the users’ multi-criteria (MC) behavioral characteristics. For example, a hotel system allows users to rate from multiple dimensions, such as environment and location (i.e., MC ratings). Moreover, selection bias is pervasive in user behavior data. Traditional data-driven methods may induce spurious association and amplified biases. To address the above challenges, we propose a debiasing framework called Multi-Criteria Causal Recommendation (MCCR), which encapsulates users’ diverse MC preferences and employs causal inference to construct novel training and inference strategies. Specifically, we first represent the causal relationships among variables in MC scenarios through the structural causal model. Then, we mitigate the negative impact of selection bias through the back-door adjustment. Next, a graph representation learning framework suitable for MC ratings is developed, which is used to extract higher-order information and infer the heterogeneity of users’ preferences with different criteria. Experimental results on six real datasets demonstrate that the MCCR significantly outperforms the existing baselines.

In point cloud, some regions typically exist nodes from multiple categories, i.e., these regions have both homophilic and heterophilic nodes. However, most existing methods ignore the heterophily of edges during the aggregation of the neighborhood node features, which inevitably mixes unnecessary information of heterophilic nodes and leads to blurred boundaries of segmentation. To address this problem, we model the point cloud as a homophilic-heterophilic graph and propose a graph regulation network (GRN) to produce finer segmentation boundaries. The proposed method can adaptively adjust the propagation mechanism with the degree of neighborhood homophily. Moreover, we build a prototype feature extraction module, which is utilised to mine the homophily features of nodes from the global prototype space. Theoretically, we prove that our convolution operation can constrain the similarity of representations between nodes based on their degree of homophily. Extensive experiments on fully and weakly supervised point cloud semantic segmentation tasks demonstrate that our method achieves satisfactory performance. Especially in the case of weak supervision, that is, each sample has only 1%-10% labeled points, the proposed method has a significant improvement in segmentation performance.

Graph Neural Networks (GNNs) have attracted significant research interest in various graph data modeling tasks. To advance trustworthy, reliable, and safe Artificial Intelligence (AI) systems for practical applications, adversarial robustness learning on GNNs has drawn widespread attention among researchers. Numerous attack methods, including white-box attacks, gray-box attacks, and black-box attacks, have been proposed, but black-box attacks are widely considered to be the most challenging and practical in real-world applications. In this paper, we focus on the challenging and realistic black-box attack scenario on GNNs, where the attacker has no information about the structure and parameters of the target model. We first theoretically demonstrate that the loss changes of the GNNs are related to the node voting matrix, which is subject to the graph topology information and is independent to the structures of GNNs. Then, we propose a novel black-box attack strategy for GNNs based on the theoretical results, i.e., node voting influence-based GNNs black-box adversarial attack, named VoteAttack. Specifically, the VoteAttack algorithm iteratively chooses a group of significant nodes based on mutual voting among nodes (the node voting matrix) and considers the voting weights among nodes. Furthermore, the VoteAttack algorithm modifies the attributes of the selected nodes to create a perturbed graph and ultimately utilizes the perturbed graph to attack GNNs. Experimental results on popular GNNs and graph datasets indicate that the proposed attack strategy outperforms baseline strategies.

Graph neural networks (GNNs) have made great progress in graph-based semi-supervised learning (GSSL). However, most existing GNNs are confronted with the oversmoothing issue that limits their expressive ability. A key factor that leads to this problem is the excessive aggregation of information from other classes when updating the node representation. To alleviate this limitation, we propose an effective method called GUIded Dropout over Edges (GUIDE) for training deep GNNs. The core of the method is to reduce the influence of nodes from other classes by removing a certain number of inter-class edges. In GUIDE, we drop edges according to the edge strength, which is defined as the time an edge acts as a bridge along the shortest path between node pairs. We find that the stronger the edge strength, the more likely it is to be an inter-class edge. In this way, GUIDE can drop more inter-class edges and keep more intra-class edges. Therefore, nodes in the same community or class are more similar, whereas different classes are more separated in the embedded space. In addition, we perform some theoretical analysis of the proposed method, which explains why it is effective in alleviating the oversmoothing problem. To validate its rationality and effectiveness, we conduct experiments on six public benchmarks with different GNNs backbones. Experimental results demonstrate that GUIDE consistently outperforms state-of-the-art methods in both shallow and deep GNNs.

Although deep learning has been extensively studied and achieved remarkable performance on single image super-resolution (SISR), existing convolutional neural networks (CNN) mainly focus on broader and deeper architecture design, ignoring the detailed information of the image itself and the potential relationship between the features. Recently, several attempts have been made to address the SISR with graph representation learning. However, existing GNN-based methods learning to deal with the SISR problem are limited to the information processing of the entire image or the relationship processing between different feature images of the same layer, ignoring the interdependence between the extracted features of different layers, which is not conducive to extracting deeper hierarchical features. In this paper, we propose an interlayer feature representation based graph neural network for image super-resolution (LSGNN), which consists of a layer feature graph representation learning module and a channel spatial attention module. The layer feature graph representation learning module mainly captures the interdependence between the features of different layers, which can learn more fine-grained image detail features. In addition, we also unified a channel attention module and a spatial attention module into our model, which takes into account the channel dimension information and spatial scale information, to improve the expressive ability, and achieve high quality image details. Extensive experiments and ablation studies demonstrate the superiority of the proposed model.

Graph convolutional neural networks can effectively process geometric data and thus have been successfully used in point cloud data representation. However, existing graph-based methods usually adopt the K-nearest neighbor (KNN) algorithm to construct graphs, which may not be optimal for point cloud analysis tasks, owning to the solution of KNN is independent of network training. In this paper, we propose a novel graph structure learning convolutional neural network (GSLCN) for multiple point cloud analysis tasks. The fundamental concept is to propose a general graph structure learning architecture (GSL) that builds long-range and short-range dependency graphs. To learn optimal graphs that best serve to extract local features and investigate global contextual information, respectively, we integrated the GSL with the designed graph convolution operator under a unified framework. Furthermore, we design the graph structure losses with some prior knowledge to guide graph learning during network training. The main benefit is that given labels and prior knowledge are taken into account in GSLCN, providing useful supervised information to build graphs and thus facilitating the graph convolution operation for the point cloud. Experimental results on challenging benchmarks demonstrate that the proposed framework achieves excellent performance for point cloud classification, part segmentation, and semantic segmentation.

Link prediction has become a significant research problem in deep learning, and the graph-based autoencoder model is one of the most important methods to solve it. The existing graph-based autoencoder models only learn a single set of distributions, which cannot accurately represent the mixed distribution in real graph data. Meanwhile, existing learning models have been greatly restricted when the graph data has insufficient attribute information and inaccurate topology information. In this paper, we propose a novel graph embedding framework, termed multi-scale variational graph autoencoder (MSVGAE), which learns multiple sets of low-dimensional vectors of different dimensions through the graph encoder to represent the mixed probability distribution of the original graph data, and performs multiple sampling in each dimension. Furthermore, a self-supervised learning strategy (i.e., graph feature reconstruction auxiliary learning) is introduced to fully use the graph attribute information to help the graph structure learning. Experiment studies on real-world graphs demonstrate that the proposed model achieves state-of-the-art performance compared with other baseline methods in link prediction tasks. Besides, the robustness analysis shows that the proposed MSVGAE method has obvious advantages in the processes of graph data with insufficient attribute information and inaccurate topology information.