Graph anomaly detection is emerging as a critical technology for addressing increasingly complex and dynamic risk environments. Although unsupervised graph anomaly detection has advanced under the graph representation learning, directly applying these paradigms remains fundamentally misaligned with anomaly detection objectives. In this work, we highlight two key insights: graph neural networks are often suboptimal as feature extractors due to neighborhood aggregation diluting anomaly signals, and reliance on local inconsistency mining is inadequate for comprehensive anomaly detection, as it often fails to identify anomalies hidden within camouflaged communities. Based on these insights, we propose multiscale inconsistency learning for graph anomaly detection (MI-GAD), a novel framework that integrates both local and global anomaly signals. Specifically, individual node representations are projected onto a common hypersphere to ensure uniformity. At the local scale, the graph structure is leveraged for affinity-aware modeling via group discrimination. At the global scale, we introduce node deviation, a metric that distinguishes anomalies by optimizing representation centers. This unified approach enables robust and comprehensive detection of diverse graph anomalies. Experiments on seven real datasets demonstrate that our method consistently outperforms state-of-the-art baselines in both effectiveness and scalability.

With the increasing scale and complexity of graph data, node attributes are also becoming richer and more complex, particularly in the form of informative text. Classic GNNs equipped with shallow attribute encoders are no longer sufficient to handle such data independently, making model collaboration across heterogeneous architectures an inevitable trend. Recently, the integration of Large Language Models (LLMs) and GNNs has attracted significant attention, yet the inherent disparity between these models remains a key challenge. Promising solutions have considered fine-tuning Small Language Models (SLMs) to bridge the gap between GNNs and frozen LLMs. However, this introduces another problem: these heterogeneous models bring complementary knowledge, but how to effectively integrate them and allow mutual refinement becomes a significant research gap. To address these challenges, we introduce COLA, a collaborative large–small model framework that enables seamless cooperation among semantic LLMs, task-specific fine-tuned SLMs, and structure-aware GNNs. COLA features a unique Consensus–Complement Coordination Mechanism (C3M), wherein its Mixture-of-Coordinators (MoC) architecturally aligns the LLM and SLM. Built upon this, a flexible graph-knowledge infusion strategy encourages the joint alignment and graph knowledge learning of textual representations. Extensive evaluations across nine diverse datasets show that COLA consistently achieves state-of-the-art performance, validating the effectiveness and generality of our collaborative paradigm.

Federated Graph Learning (FGL) has emerged as a compelling paradigm for collaboratively training a global model while preserving the privacy of multi-source graphs. Nonetheless, FGL faces a critical challenge of data heterogeneity, where semantic and structural discrepancies across clients significantly degrade its performance. Although existing methods attempt to calibrate client-specific graph distributions during federated training, they inevitably fall short in aligning the optimization behaviors across clients due to dynamic parameter updates, thereby inducing a bottleneck in generalization improvement. To tackle this challenge, we propose a solution from a new perspective of prior refinement, which seeks to proactively harmonize client graph distributions before the federated training. In particular, we propose a Federated Graph Harmonization (FedGH) framework that exploits the generative strengths of graph diffusion models to perform prior refinement of local graphs. In a nutshell, FedGH designs a conditional diffusion mechanism on each client that synthesizes pseudo-graphs encapsulating both feature and structural priors, thereby facilitating explicit correction of inter-client distributional bias. On the server side, we employ the graph contrastive learning between various client-specific pseudo-graphs to incorporate the global information, subsequently guiding local data reconstruction. Importantly, model-agnostic FedGH can be seamlessly deployed as a plug-and-play module to be easily integrated with existing FGL architectures. Extensive experiments demonstrate that FedGH consistently outperforms state-of-the-art FGL baselines.

Graph Neural Networks (GNNs) have exhibited remarkable capabilities for dealing with graph-structured data. However, recent studies have revealed their fragility to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions. To enhance adversarial robustness, some methods attempt to learn robust representation through improving GNN architectures. Subsequently, another approach suggests that these GNNs might taint feature information and have poor classifier performance, leading to the introduction of Graph Contrastive Learning (GCL) methods to build a refining-classifying pipeline. However, existing methods focus on global-local contrastive strategies, which fails to address the robustness issues inherent in the contexts of adversarial robustness. To address these challenges, we propose a novel paradigm named GRANCE to enhance the robustness of learned representations by shifting the focus to local neighborhoods. Specifically, a dual neighborhood contrastive learning strategy is designed to extract local topological and semantic information. Paired with a neighbor estimator, the strategy can learn robust representations that are resilient to adversarial edges. Additionally, we also provide an improved GNN as classifier. Theoretical analyses provide a stricter lower bound of mutual information, ensuring the convergence of GRANCE. Extensive experiments validate the effectiveness of GRANCE compared to state-of-the-art baselines against various adversarial attacks.

Integrating multi-omics datasets through data-driven analysis offers a comprehensive understanding of the complex biological processes underlying various diseases, particularly cancer. Graph Neural Networks (GNNs) have recently demonstrated remarkable ability to exploit relational structures in biological data, enabling advances in multi-omics integration for cancer subtype classification. Existing approaches often neglect the intricate coupling between heterogeneous omics, limiting their capacity to resolve subtle cancer subtype heterogeneity critical for precision oncology. To address these limitations, we propose a framework named Graph Transformer for Multi-omics Cancer Subtype Classification (GTMancer). This framework builds upon the GNN optimization problem and extends its application to complex multi-omics data. Specifically, our method leverages contrastive learning to embed multi-omics data into a unified semantic space. We unroll the multiplex graph optimization problem in that unified space and introduce dual sets of attention coefficients to capture structural graph priors both within and among multi-omics data. This approach enables global omics information to guide the refining of the representations of individual omics. Empirical experiments on seven real-world cancer datasets demonstrate that GTMancer outperforms existing state-of-the-art algorithms.

Graph learning has shown significant advantages in organizing and leveraging complex data, making it promising for numerous real-world applications with heterogeneous information, particularly multi-omics data analysis. Despite its potential in such scenarios, existing methods are still in their infancy, lacking architectural potential and struggling to handle such complex data. In this paper, we propose the Multiplex Mixture of Graph Learners (MMoG) framework. MMoG first conducts fine-grained processing of consensus and unique information, constructing consistent features and multiplex graph structures. Then, a macroscopically shared group of sub-GNNs with diverse orders and architectures synergistically learn representations, providing a foundation for strong interaction between different views. Inspired by the mixture of experts (MoE), each sample in different omics adaptively determines the neighborhood ranges and architectures for information aggregation, while blocking unsuitable sub-GNNs. MMoG treats the complex multi-omics analysis as a multi-view learning problem, and essentially decomposes it into multiple sub-problems, allowing each omics/view to solve intersecting yet unique sub-problem groups. Additionally, we introduce mutual information-driven orthogonal loss and balancing loss to avoid view collapse. Extensive experiments on multi-omics data across multiple cancer types highlight MMoG's superiority.

Graph Neural Networks (GNNs) have become powerful models for both node- and graph-level tasks. While node-level learning focuses on individual nodes and their local structures, graph-level learning encounters challenges in capturing the global properties of graphs. In this paper, we conduct a theoretical and experimental analysis of existing graph-level learning frameworks and find that these frameworks typically adopt a single-view perspective based solely on node degree, which limits their ability to capture comprehensive graph characteristics. To address these issues, we propose a multi-view approach that leverages different types of centrality measures to capture diverse aspects of graph structure. We design an attention-based mechanism to adaptively integrate these multiple views, and use it as a readout function to perform weighted summation of node embeddings, termed as Adaptive Centrality Readout (ACRead). ACRead demonstrates enhanced flexibility and effectiveness when integrated with various GNN architectures, outperforming state-of-the-art readout methods, including KerRead and Set Transformer. Additionally, this multi-view centrality approach can serve as a standalone graph-level learning framework without relying on GNNs, referred to as Adaptive Centrality-based Graph Learning (ACGL), which achieves competitive performance by effectively combining different centrality perspectives.

Facing the growing diversity of multi-view data, multi-view graph-based models have made encouraging progress in handling multi-view data modeled as graphs. Graph Contrastive Learning (GCL) naturally fits multi-view graph data by treating their inherent views as augmentations. However, the development of GCL on multi-view graph data is still in the infant stage. Challenges remain in designing strategies that coordinate preprocessing and contrastive learning, and in developing model architectures that automatically meet the needs of diverse views. To tackle these, we propose a framework named CAMEL, which refines consistency learning by introducing a tailored contrastive paradigm for multi-view graphs. Initially, we theoretically analyze the positive effect of edge-dropping preprocessing on the consistency and quantify the factors that influence it. Paired with a learnable model architecture, the proposed adaptive edge-dropping preprocessing strategy is guided by dynamic topology, making the heterogeneity of views more controllable and better aligned with contrastive learning. Finally, we design a neighborhood consistency multi-view contrastive objective that enhances consistency information interaction by extending positive samples. Extensive experiments on downstream tasks, including node classification and clustering, validate the superiority of our proposed model.

Time series anomaly detection has garnered significant research attention due to growing demands for temporal data monitoring across diverse domains. Despite the rapid advent of unsupervised anomaly detection models, existing approaches face two critical challenges in understanding the mechanisms of reconstruction-based models when handling diverse temporal dependencies: (1) the insufficient exploration of complex inter-timestamp relationships encompassing both short-term and long-term dependencies, and (2) the lack of integrated frameworks for jointly learning short-term patterns and long-term temporal characteristics. To address these challenges, we propose the novel Multi-Scale Hypergraph Transformer (MSHTrans), which leverages the capacity of hypergraphs for modeling multi-order temporal dependencies. Particularly, our method employs multi-scale downsampling to derive complementary fine-grained and coarse-grained representations, integrated with trainable hypergraph neural networks that can adaptively learn inter-timestamp relationships. The framework further integrates time series decomposition to systematically extract periodic and trend components from multi-granular features, thereby enhancing long-term dependency modeling. Through synergistic integration of learned short-term patterns and long-term temporal structures, the model achieves comprehensive time series reconstruction for effective anomaly detection. Extensive experiments demonstrate that MSHTrans outperforms state-of-the-art competitors with an average performance improvement of 8.21% (without point adjustment) and 3.52% (with point adjustment).

In recent years, graph-based multi-view learning has received widespread attention for its ability to utilize data dependencies to capture more comprehensive information from ubiquitous multi-view data. However, with the increase in data size and complexity, Euclidean space struggles to capture the hierarchical and exponentially expanding relationships of multi-view data in limited dimensions, leading to embedding distortion and insufficient cross-view alignment and interaction. To this end, we propose a hyperbolic multi-view heat diffusion method. Firstly, we utilize the negative curvature advantage of the hyperbolic space to construct a graph representation for each view separately, so that each view can still retain its hierarchical structure in relatively low dimensions. Then we construct a graph heat diffusion process on hyperbolic manifolds to ensure that each view is locally smoothed and globally aggregated while achieving semantic consistency through virtual views. We show that the method can be interpreted as a Riemannian gradient descent process for collaborative learning on hyperbolic manifolds, which not only effectively fuses multimodal information, but also significantly enhances the interaction and unified representation among different views. Experimental results show that the proposed framework achieves excellent performance in a variety of multi-view scenarios.

The convergence of graph learning and multi-view learning has propelled the emergence of multi-view graph neural networks (MGNNs), offering strong capabilities to address complex real-world data characterized by heterogeneous yet interconnected information. While existing MGNNs exploit the potential of multi-view graphs, the inherent conflict persists between the two critical inductive biases of multi-view learning, consistency and complementarity. Consequently, the challenge of defining and resolving this tension in the new context of multi-view graphs remains largely underexplored. To bridge this gap, we propose Multi-view Collaborative Graph Experts (MvCGE), a novel framework grounded in the Mixture-of-Experts (MoE) paradigm. MvCGE establishes architectural consistency through shared parameters while preserving complementarity via layer-wise collaborative graph experts, which are dynamically activated by a graph-aware routing mechanism that adapts to the structural nuances of each view. This dual-level design is further reinforced by two novel components: a load equilibrium loss to prevent expert collapse and ensure balanced specialization, and a graph discrepancy loss based on distributional divergence to enhance inter-view complementarity. Extensive experiments on diverse datasets demonstrate MvCGE’s superiority.

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edge-dropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness.

Graph convolutional network (GCN) has gained widespread attention in semisupervised classification tasks. Recent studies show that GCN-based methods have achieved decent performance in numerous fields. However, most of the existing methods generally adopted a fixed graph that cannot dynamically capture both local and global relationships. This is because the hidden and important relationships may not be directed exhibited in the fixed structure, causing the degraded performance of semisupervised classification tasks. Moreover, the missing and noisy data yielded by the fixed graph may result in wrong connections, thereby disturbing the representation learning process. To cope with these issues, this article proposes a learnable GCN-based framework, aiming to obtain the optimal graph structures by jointly integrating graph learning and feature propagation in a unified network. Besides, to capture the optimal graph representations, this article designs dual-GCN-based meta-channels to simultaneously explore local and global relations during the training process. To minimize the interference of the noisy data, a semisupervised graph information bottleneck (SGIB) is introduced to conduct the graph structural learning (GSL) for acquiring the minimal sufficient representations. Concretely, SGIB aims to maximize the mutual information of both the same and different meta-channels by designing the constraints between them, thereby improving the node classification performance in the downstream tasks. Extensive experimental results on real-world datasets demonstrate the robustness of the proposed model, which outperforms state-of-the-art methods with fixed-structure graphs.

Facing the increasing heterogeneity of data in the real world, multi-view learning has become a crucial area of research. Graph Convolutional Networks (GCNs) are powerful for modeling both graph structures and features, making them a focal point in multi-view learning research. However, these methods typically only account for static data dependencies within each view separately when constructing the topology necessary for GCNs, overlooking potential relationships across views in multi-view data. Furthermore, there is a notable absence of theoretical guidance for constructing multi-view data topologies, leading to uncertainty regarding the progression of graph embeddings toward a consistent state. To tackle these challenges, we introduce a framework named energy-constrained multi-view graph diffusion. This approach establishes a mathematical correspondence between multi-view data and GCNs via graph diffusion. It treats multi-view data as a unified entity and devises a feature propagation process with inter-view awareness by considering both inter-view and intra-view feature flow across the entire system. Additionally, an energy function is introduced to guide the inter- and intra-view diffusion, ensuring that the representations converge towards global consistency. The empirical research on several benchmark datasets substantiates the benefits of the proposed method.

With the growing diversity of data sources, multi-view learning methods have attracted considerable attention. Among these, by modeling the multi-view data as multi-view graphs, multi-view Graph Neural Networks (GNNs) have shown encouraging performance on various multi-view learning tasks. The message passing is the critical mechanism empowering GNNs with superior capacity to process complex graph data. However, most multi-view GNNs are designed on the well-established overall framework, overlooking the intrinsic challenges of the message passing on multi-view scenarios. To clarify this, we first revisit the message passing mechanism from a Laplacian smoothing perspective, revealing the key to designing a multi-view message passing. Following the analysis, in this paper, we propose an enhanced GNN framework termed Confluent Graph Neural Networks (CGNN), with Cross-view Confulent Message Pssing (CCMP) tailored for multi-view learning. Inspired by the optimization of an improved multi-view Laplacian smoothing problem, CCMP contains three sub-modules that enable the interaction between graph structures and consistent representations, which makes it aware of consistency and complementarity information across views. Extensive experiments on four types of data including multi-modality data demonstrate that our proposed model exhibits superior effectiveness and robustness. The code is available at https://github.com/shumanzhuang/CGNN.

Graph Convolutional Network (GCN) has drawn widespread attention in data mining on graphs due to its outstanding performance and rigor theoretical guarantee. However, some recent studies have revealed that GCN-based methods may mine latent information insufficiently owing to the underutilization of the feature space . Besides, the unlearnable topology also significantly imperils the performance of GCN-based methods. In this paper, we conduct experiments to investigate these issues, finding that GCN does not fully consider the potential structure in the feature space, and a fixed topology deteriorates the robustness of GCN. Thus, it is desired to distill node features and establish a learnable graph. Motivated by this goal, we propose a framework dubbed G raph C onvolutional N etwork with e lastic t opology (GCNet 1 ). With the analysis of the optimization for the proposed flexible Laplacian embedding, GCNet is naturally constructed by alternative graph convolutional layers and adaptive topology learning layers. GCNet aims to deeply explore the feature space and employ the mined information to construct a learnable topology, which leads to a more robust graph representation. In addition, a set-level orthogonal loss is utilized to meet the orthogonal constraint required by the flexible Laplacian embedding and promote better class separability . Moreover, comprehensive experiments indicate that GCNet achieves remarkable performance and generalization on several real-world datasets.

Existing multi-view graph learning methods often rely on consistent information for similar nodes within and across views, however they may lack adaptability when facing diversity challenges from noise, varied views, and complex data distributions. These challenges can be mainly categorized into: 1) View-specific diversity within intra-view from noise and incomplete information; 2) Cross-view diversity within inter-view caused by various latent semantics; 3) Cross-group diversity within inter-group due to data distribution differences. To this end, we propose a universal multi-view consensus graph learning framework that considers both original and generative graphs to balance consistency and diversity. Specifically, the proposed framework can be divided into the following four modules: i) Multi-channel graph module to extract principal node information, ensuring view-specific and cross-view consistency while mitigating view-specific and cross-view diversity within original graphs; ii) Generative module to produce cleaner and more realistic graphs, enriching graph structure while maintaining view-specific consistency and suppressing view-specific diversity; iii) Contrastive module to collaborate on generative semantics to facilitate cross-view consistency and reducing cross-view diversity within generative graphs; iv) Consensus graph module to consolidate learning a consensual graph, pursuing cross-group consistency and cross-group diversity. Extensive experimental results on real-world datasets demonstrate its effectiveness and superiority.

Multi-view learning is a promising research field that aims to enhance learning performance by integrating information from diverse data perspectives. Due to the increasing interest in graph neural networks, researchers have gradually incorporated various graph models into multi-view learning. Despite significant progress, current methods face challenges in extracting information from multiple graphs while simultaneously accommodating specific downstream tasks. Additionally, the lack of a subsequent refinement process for the learned graph leads to the incorporation of noise. To address the aforementioned issues, we propose a method named generative essential graph convolutional network for multi-view semi-supervised classification. Our approach integrates the extraction of multi-graph consistency and complementarity, graph refinement, and classification tasks within a comprehensive optimization framework. This is accomplished by extracting a consistent graph from the shared representation, taking into account the complementarity of the original topologies. The learned graph is then optimized through downstream-specific tasks. Finally, we employ a graph convolutional network with a learnable threshold shrinkage function to acquire the graph embedding. Experimental results on benchmark datasets demonstrate the effectiveness of our approach.

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multilayer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an alternating graph-regularized neural network (AGNN) composed of graph convolutional layer (GCL) and graph embedding layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multilayer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with the state-of-the-art models.

Due to the powerful capability to gather the information of neighborhood nodes, Graph Convolutional Network (GCN) has become a widely explored hotspot in recent years. As a well-established extension, Graph AutoEncoder (GAE) succeeds in mining underlying node representations via evaluating the quality of adjacency matrix reconstruction from learned features. However, limited works on GAE were devoted to leveraging both semantic and topological graphs, and they only indirectly extracted the relationships between graphs via weights shared by features. To better capture the connections between nodes from these two types of graphs, this paper proposes a graph neural network dubbed Dual Low-Rank Graph AutoEncoder (DLR-GAE), which takes both semantic and topological homophily into consideration. Differing from prior works that share common weights between GCNs, the presented DLR-GAE conducts sustained exploration of low-rank information between two distinct graphs, and reconstructs adjacency matrices from learned latent factors and embeddings. In order to obtain valid adjacency matrices that meet certain conditions, we design some surrogates and projections to restrict the learned factor matrix. We compare the proposed model with state-of-the-art methods on several datasets, which demonstrates the superior accuracy of DLR-GAE in semi-supervised classification.

Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. However, few work provide a general view to interpret various GCNs and guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we leverage the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.

Multi-omics data provides a wealth of information concerning disease mechanisms, which benefits the exploration of the intricate molecular phenomena underlying diseases. In recent years, considerable endeavors have been directed towards the combination of graph convolutional network, which has the powerful ability to gather information, with multi-omics learning methods to obtain more reliable results. For achieving this pursuit, an essential challenge is data integration. Against this backdrop, we propose a unified framework named multi-level knowledge integration with graph convolutional network, which effectively incorporates multiple prior knowledge and omics data to learn an intrinsic representation. In specific, the model consists of two subnetworks: an attribute-level module and a sample-level module. The former firstly aggregates the knowledge given by the prior biological graphs into low-dimensional embeddings, and then maximizes the consistency between these prior views via optimizing a contrastive loss for attaining the attribute-based representations. The latter leverages an encoder to dimensionalize the original multi-omics data to attain more dominant sample knowledge, and subsequently utilizes another contrastive loss to align these representations between multiple omics for learning the global sample-level information. Comprehensive experiments are performed to show that the proposed model surpasses other state-of-the-art methods.

As real-world data become increasingly heterogeneous, multi-view semi-supervised learning has garnered widespread attention. Although existing studies have made efforts towards this and achieved decent performance, they are restricted to shallow models and how to mine deeper information from multiple views remains to be investigated. As a recently emerged neural network, Graph Convolutional Network (GCN) exploits graph structure to propagate label signals and has achieved encouraging performance, and it has been widely employed in various fields. Nonetheless, research on solving multi-view learning problems via GCN is limited and lacks interpretability. To address this gap, in this paper we propose a framework termed Interpretable Multi-view Graph Convolutional Network (IMvGCN11Code is available athttps://github.com/ZhihaoWu99/IMvGCN.). We first combine the reconstruction error and Laplacian embedding to formulate a multi-view learning problem that explores the original space from feature and topology perspectives. In light of a series of derivations, we establish a potential connection between GCN and multi-view learning, which holds significance for both domains. Furthermore, we propose an orthogonal normalization method to guarantee the mathematical connection, which solves the intractable problem of orthogonal constraints in deep learning. In addition, the proposed framework is applied to the multi-view semi-supervised learning task. Comprehensive experiments demonstrate the superiority of our proposed method over other state-of-the-art methods.