Multi-view Graph Research Articles

Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency.

Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on 12 multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. The code is available at https://github.com/youweiliang/Multi-view_Graph_Learning.

Multi-View Comprehensive Graph Clustering

Multi-view clustering algorithms have been successfully used in different consumer electronic products, such as common digital cameras and unmanned vehicles. Currently, existing multi-view graph clustering (MVGC) methods learn the similarity of directly connected samples for clustering. However, these MVGC methods can not fully consider the indirect relation among samples and high-order relation across multi-view data. In this paper, a new multi-view comprehensive graph clustering (MCGC) method is devised, which can fully learn the similarity based on (1) first-order proximity (FOP) (i.e. the direct relation of pairwise samples); (2) second-order proximity (SOP) (i.e. the indirect relation of pairwise samples); and (3) third-order proximity (TOP) (i.e. the three-order relation of multiple views). Since the operations of these three components are iteratively carried out, the interaction between similarity learning can be encouraged and the comprehensive graph can be generated effectively for clustering. In-depth experiments on six commonly benchmark datasets show the superiority of the MCGC method.

PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. In this study, we reorganized a fundamental dataset of parasitic disease-drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-view heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from each view and fed them into the ultimate discriminator. The experimental results demonstrate that PDDGCN outperforms five state-of-the-art methods and four compared machine learning algorithms. Additionally, case studies have substantiated the effectiveness of PDDGCN in identifying associations between parasitic diseases and drugs. In summary, the PDDGCN model has the potential to facilitate the discovery of potential treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available at https://github.com/AhauBioinformatics/PDDGCN .

Knowledge reasoning in power grid infrastructure projects based on deep multi-view graph convolutional network

With the rapid development of power grid infrastructure, especially the increasing number of ultra-high voltage (UHV) projects, knowledge extracted from historical engineering data is collected and can be potentially used to assist in the review of power transmission and transformation projects. However, conventional knowledge modeling and knowledge reasoning methods cannot meet the current needs of power grid construction. In this paper, considering the more supernumerary and distinctive information brought by multi-view data which could be beneficial for feature representation and knowledge reasoning from the constructed knowledge base, a multi-view graph convolutional network (GCN) based on knowledge graph is proposed to make classification for power grid infrastructure projects. Specifically, several views are constructed based on attribute information of a knowledge graph. In addition, a Haar convolution-based pooling mechanism is employed to capture the structural features represented by a chain of subgraphs. And then an aggregator that combines both attribute and structural information is used to classify UHV projects. Results from both UHV and NCI-1 datasets indicate that our proposed method is more has higher accuracy and generalization ability.

Cross-View Approximation on Grassmann Manifold for Multiview Clustering.

In existing multiview clustering research, the comprehensive learning from multiview graph and feature spaces simultaneously remains insufficient when achieving a consistent clustering structure. In addition, a postprocessing step is often required. In light of these considerations, a cross-view approximation on Grassman manifold (CAGM) model is proposed to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources. The model uses a ratio-formed objective function, enabling parameter-free bidirectional fusion. Furthermore, the CAGM model incorporates a paired encoding mechanism to generate low-dimensional and orthogonal cross-view embeddings. Through the approximation of two measurable subspaces on the Grassmann manifold, the direct acquisition of the indicator matrix is realized. Furthermore, an effective optimization algorithm corresponding to the CAGM model is derived. Comprehensive experiments on four real-world datasets are conducted to substantiate the effectiveness of our proposed method.

Multi-view Graph Convolutional Network with Spectral Component Decompose for Remote Sensing Images Classification

Automatic land cover classification from high-resolution remote sensing (RS) images remains challenging due to the complex composition of classes. Given the potential of a graph to simulate latent class composition, the latest development of graph convolutional network (GCN) has received increasing attention. However, most existing methods only use a single perspective graph structure, which largely limits their ability to capture the complementary features that would better represent the underlying data structure of images. Therefore, this paper proposes a novel multi-view GCN-based representation learning network(MvRLNet) for RS image classification. First, a superpixel-based spectral component decomposes module(SSCDM) is designed to enhance the uniqueness and homogeneity of graph nodes because the mixed superpixels may lead to miscellaneous information on graph aggregations. Second, a multi-view graph learning module(MGLM) is proposed to integrate topology and spectral graph information into a unified network with an effective feature learning strategy. Finally, the effectiveness of the proposed MvRLNet is validated on a variety datasets with different resolutions. The experimental results show that the proposed MvRLNet performs better than state-of-the-art techniques.

Adaptive Multiview Graph Convolutional Network for 3D Point Cloud Classification and Segmentation

Adaptive Multiview Graph Convolutional Network for 3D Point Cloud Classification and Segmentation

Tensor Representation Based Multi-View Graph Contrastive Learning for IoE Intelligence

Tensor Representation Based Multi-View Graph Contrastive Learning for IoE Intelligence

UMCGL: Universal Multi-View Consensus Graph Learning With Consistency and Diversity.

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.

Identifying Associations between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism.

Research has shown that small nucleolar RNAs (snoRNAs) play crucial roles in various biological processes, and understanding disease pathogenesis by studying their relationship with diseases is beneficial. Currently, known associations are insufficient, and conventional biological experiments are costly and time-consuming. Therefore, developing efficient computational methods is crucial for identifying potential snoRNA-disease associations. In this paper, a method to identify snoRNA-disease associations based on graph convolutional network and multi-view graph attention mechanism (GCASDA) is proposed. Firstly, the similarity matrices of snoRNAs and diseases are calculated based on biological entity-related information, and the weights of the edges between the snoRNA nodes and the disease nodes are supplemented by random forest. Then two homogeneous graphs and one heterogeneous graph are constructed. Subsequently, different types of embedded features are extracted from the graphs using specific graph convolutional network structure and integrated through a multi-view graph attention mechanism to obtain node embedded feature representations. Finally, for each pair of nodes, in addition to their global features, node interaction features are passed together to a multilayer perceptron neural network (MLP) to identify snoRNA-disease associations. Experimental results show that GCASDA achieves 0.9356 and 0.9294 in AUC and AUPR, respectively, and significantly outperformed other state-of-the-art methods on the basis of different evaluation metrics. Furthermore, the case study could further demonstrate the realistic feasibility of GCASDA.

Clustering Enhanced Multiplex Graph Contrastive Representation Learning.

Multiplex graph representation learning has attracted considerable attention due to its powerful capacity to depict multiple relation types between nodes. Previous methods generally learn representations of each relation-based subgraph and then aggregate them into final representations. Despite the enormous success, they commonly encounter two challenges: 1) the latent community structure is overlooked and 2) consistent and complementary information across relation types remains largely unexplored. To address these issues, we propose a clustering-enhanced multiplex graph contrastive representation learning model (CEMR). In CEMR, by formulating each relation type as a view, we propose a multiview graph clustering framework to discover the potential community structure, which promotes representations to incorporate global semantic correlations. Moreover, under the proposed multiview clustering framework, we develop cross-view contrastive learning and cross-view cosupervision modules to explore consistent and complementary information in different views, respectively. Specifically, the cross-view contrastive learning module equipped with a novel negative pairs selecting mechanism enables the view-specific representations to extract common knowledge across views. The cross-view cosupervision module exploits the high-confidence complementary information in one view to guide low-confidence clustering in other views by contrastive learning. Comprehensive experiments on four datasets confirm the superiority of our CEMR when compared to the state-of-the-art rivals.

Scalable and Structural Multi-View Graph Clustering With Adaptive Anchor Fusion.

Anchor graph has been recently proposed to accelerate multi-view graph clustering and widely applied in various large-scale applications. Different from capturing full instance relationships, these methods choose small portion anchors among each view, construct single-view anchor graphs and combine them into the unified graph. Despite its efficiency, we observe that: (i) Existing mechanism adopts a separable two-step procedure-anchor graph construction and individual graph fusion, which may degrade the clustering performance. (ii)These methods determine the number of selected anchors to be equal among all the views, which may destruct the data distribution diversity. A more flexible multi-view anchor graph fusion framework with diverse magnitudes is desired to enhance the representation ability. (iii) During the latter fusion process, current anchor graph fusion framework follows simple linearly-combined style while the intrinsic clustering structures are ignored. To address these issues, we propose a novel scalable and flexible anchor graph fusion framework for multi-view graph clustering method in this paper. Specially, the anchor graph construction and graph alignment are jointly optimized in our unified framework to boost clustering quality. Moreover, we present a novel structural alignment regularization to adaptively fuse multiple anchor graphs with different magnitudes. In addition, our proposed method inherits the linear complexity of existing anchor strategies respecting to the sample number, which is time-economical for large-scale data. Experiments conducted on various benchmark datasets demonstrate the superiority and effectiveness of the newly proposed anchor graph fusion framework against the existing state-of-the-arts over the clustering performance promotion and time expenditure. Our code is publicly available at https://github.com/wangsiwei2010/SMVAGC-SF.

Multiview Representation Learning With One-to-Many Dynamic Relationships.

Integrating information from multiple views to obtain potential representations with stronger expressive ability has received significant attention in practical applications. Most existing algorithms usually focus on learning either the consistent or complementary representation of views and, subsequently, integrate one-to-one corresponding sample representations between views. Although these approaches yield effective results, they do not fully exploit the information available from multiple views, limiting the potential for further performance improvement. In this article, we propose an unsupervised multiview representation learning method based on sample relationships, which enables the one-to-many fusion of intraview and interview information. Due to the heterogeneity of views, we need mainly face the two following challenges: 1) the discrepancy in the dimensions of data across different views and 2) the characterization and utilization of sample relationships across these views. To address these two issues, we adopt two modules: the dimension consistency relationship enhancement module and the multiview graph learning module. Thereinto, the relationship enhancement module addresses the discrepancy in data dimensions across different views and dynamically selects data dimensions for each sample that bolsters intraview relationships. The multiview graph learning module devises a novel multiview adjacency matrix to capture both intraview and interview sample relationships. To achieve one-to-many fusion and obtain multiview representations, we employ the graph autoencoder structure. Furthermore, we extend the proposed architecture to the supervised case. We conduct extensive experiments on various real-world multiview datasets, focusing on clustering and multilabel classification tasks, to evaluate the effectiveness of our method. The results demonstrate that our approach significantly improves performance compared to existing methods, highlighting the potential of leveraging sample relationships for multiview representation learning. Our code is released at https://github.com/ lilidan-orm/one-to-many-multiview on GitHub.

Self-supervised Pre-training via Multi-view Graph Information Bottleneck for Molecular Property Prediction.

Molecular representation learning has remarkably accelerated the development of drug analysis and discovery. It implements machine learning methods to encode molecule embeddings for diverse downstream drug-related tasks. Due to the scarcity of labeled molecular data, self-supervised molecular pre-training is promising as it can handle large-scale unlabeled molecular data to prompt representation learning. Although many universal graph pre-training methods have been successfully introduced into molecular learning, there still exist some limitations. Many graph augmentation methods, such as atom deletion and bond perturbation, tend to destroy the intrinsic properties and connections of molecules. In addition, identifying subgraphs that are important to specific chemical properties is also challenging for molecular learning. To address these limitations, we propose the self-supervised Molecular Graph Information Bottleneck (MGIB) model for molecular pre-training. MGIB observes molecular graphs from the atom view and the motif view, deploys a learnable graph compression process to extract the core subgraphs, and extends the graph information bottleneck into the self-supervised molecular pre-training framework. Model analysis validates the contribution of the self-supervised graph information bottleneck and illustrates the interpretability of MGIB through the extracted subgraphs. Extensive experiments involving molecular property prediction, including 7 binary classification tasks and 6 regression tasks demonstrate the effectiveness and superiority of our proposed MGIB.

Multi-View Graph Convolution Network Reinforcement Learning for CAVs Cooperative Control in Highway Mixed Traffic

Multi-View Graph Convolution Network Reinforcement Learning for CAVs Cooperative Control in Highway Mixed Traffic

Unsupervised Multiview Graph Contrastive Feature Learning for Hyperspectral Image Classification

Unsupervised Multiview Graph Contrastive Feature Learning for Hyperspectral Image Classification

Unpaired Multi-View Graph Clustering With Cross-View Structure Matching.

Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are predefined or given in advance. However, the data correspondence is often incomplete in real-world applications due to data corruption or sensor differences, referred to as the data-unpaired problem (DUP) in multi-view literature. Although several attempts have been made to address the DUP issue, they suffer from the following drawbacks: 1) most methods focus on the feature representation while ignoring the structural information of multi-view data, which is essential for clustering tasks; 2) existing methods for partially unpaired problems rely on pregiven cross-view alignment information, resulting in their inability to handle fully unpaired problems; and 3) their inevitable parameters degrade the efficiency and applicability of the models. To tackle these issues, we propose a novel parameter-free graph clustering framework termed unpaired multi-view graph clustering framework with cross-view structure matching (UPMGC-SM). Specifically, unlike the existing methods, UPMGC-SM effectively utilizes the structural information from each view to refine cross-view correspondences. Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering. Moreover, existing graph clustering methods can adopt our UPMGC-SM to enhance their ability for unpaired scenarios. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both paired and unpaired datasets.

Multi-gate Mixture of Multi-view Graph Contrastive Learning on Electronic Health Record.

Electronic Health Record (EHR) is the digital form of patient visits containing various medical data, including diagnosis, treatment, and lab events. Representation learning of EHR with deep learning methods has been beneficial for patient-related prediction tasks. Recently, studies have focused on revealing the inherent graph structure between medical events in EHR. Graph neural network (GNN) methods are prevalent and perform well in various prediction tasks. However, the inherent relationships between various medical events must be marked, which is complicated and time-consuming. Most research works adopt the straightforward structure of GNN models on a single prediction task which could not fully exploit the potential of EHR representations. Compared with previous work, the multi-task prediction could utilize the latent information of concealed correlations between different prediction tasks. In addition, self-contrastive learning on graphs could improve the representation learned by GNN. We propose a multi-gate mixture of multi-view graph contrastive learning (MMMGCL) method, aiming to get a more reasonable EHR representation and improve the performances of downstream tasks. First, each patient visit is represented as a graph with a well-designed hierarchically fully-connected pattern. Second, node features in the manually constructed graph are pre-trained via the Glove method with hierarchical ontology knowledge. Finally, MMMGCL processes the pre-trained graph and adopts a joint learning strategy to simultaneously optimize task and contrastive losses. We verify our method on two large open-source medical datasets, Medical Information Mart for Intensive Care (MIMIC-III) and the eICU Collaborative Research Database (eICU). Experiment results show that our method could improve performance compared to straightforward graph-based methods on prediction tasks of patient readmission, mortality, and length of stay.

CLEAR: Cluster-Enhanced Contrast for Self-Supervised Graph Representation Learning.

This article studies self-supervised graph representation learning, which is critical to various tasks, such as protein property prediction. Existing methods typically aggregate representations of each individual node as graph representations, but fail to comprehensively explore local substructures (i.e., motifs and subgraphs), which also play important roles in many graph mining tasks. In this article, we propose a self-supervised graph representation learning framework named cluster-enhanced Contrast (CLEAR) that models the structural semantics of a graph from graph-level and substructure-level granularities, i.e., global semantics and local semantics, respectively. Specifically, we use graph-level augmentation strategies followed by a graph neural network-based encoder to explore global semantics. As for local semantics, we first use graph clustering techniques to partition each whole graph into several subgraphs while preserving as much semantic information as possible. We further employ a self-attention interaction module to aggregate the semantics of all subgraphs into a local-view graph representation. Moreover, we integrate both global semantics and local semantics into a multiview graph contrastive learning framework, enhancing the semantic-discriminative ability of graph representations. Extensive experiments on various real-world benchmarks demonstrate the efficacy of the proposed over current graph self-supervised representation learning approaches on both graph classification and transfer learning tasks.

A Multi-View Graph Contrastive Learning Framework for Defending Against Adversarial Attacks

A Multi-View Graph Contrastive Learning Framework for Defending Against Adversarial Attacks