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Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell‐cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low‐dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial‐to‐mesenchymal transition and PI3K/AKT signaling in specific sub‐regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder‐based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes. [ABSTRACT FROM AUTHOR]

Temporal action detection, a critical task in video activity understanding, is typically divided into two stages: proposal generation and classification. However, most existing methods overlook the importance of information transfer among proposals during classification, often treating each proposal in isolation, which hampers accurate label prediction. In this article, we propose a novel method for inferring semantic relationships both within and between action proposals, guiding the fusion of action proposal features accordingly. Building on this approach, we introduce the Proposal Semantic Relationship Graph Network (PSRGN), an end-to-end model that leverages intra-proposal semantic relationship graphs to extract cross-scale temporal context and an inter-proposal semantic relationship graph to incorporate complementary neighboring information, significantly improving proposal feature quality and overall detection performance. This is the first method to apply graph structure learning in temporal action detection, adaptively constructing the inter-proposal semantic graph. Extensive experiments on two datasets demonstrate the effectiveness of our approach, achieving state-of-the-art (SOTA). Code and results are available at http://github.com/Riiick2011/PSRGN. [ABSTRACT FROM AUTHOR]

The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)–based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN). [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital tool for guaranteeing the success of IIoT deployments. In light of the distinctive features of the IIoT, graph-based anomaly detection emerges as a method with great potential. However, traditional graph neural networks, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have certain limitations and significant room for improvement. Moreover, previous anomaly detection methods based on graph neural networks have focused only on capturing dependencies in the spatial dimension, lacking the ability to capture dynamics in the temporal dimension. To address these shortcomings, we propose an anomaly detection method based on Spatio-Temporal Gated Attention Networks (STGaAN). STGaAN learns a graph structure representing the dependencies among sensors and then utilizes gated graph attention networks and temporal convolutional networks to grasp the spatio-temporal connections in time series data of sensors. Furthermore, STGaAN optimizes the results jointly based on both reconstruction and prediction loss functions. Experiments on public datasets indicate that STGaAN performs better than other advanced baselines. We also visualize the learned graph structures to provide insights into the effectiveness of graph-level anomaly detection. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital tool for guaranteeing the success of IIoT deployments. In light of the distinctive features of the IIoT, graph-based anomaly detection emerges as a method with great potential. However, traditional graph neural networks, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have certain limitations and significant room for improvement. Moreover, previous anomaly detection methods based on graph neural networks have focused only on capturing dependencies in the spatial dimension, lacking the ability to capture dynamics in the temporal dimension. To address these shortcomings, we propose an anomaly detection method based on Spatio-Temporal Gated Attention Networks (STGaAN). STGaAN learns a graph structure representing the dependencies among sensors and then utilizes gated graph attention networks and temporal convolutional networks to grasp the spatio-temporal connections in time series data of sensors. Furthermore, STGaAN optimizes the results jointly based on both reconstruction and prediction loss functions. Experiments on public datasets indicate that STGaAN performs better than other advanced baselines. We also visualize the learned graph structures to provide insights into the effectiveness of graph-level anomaly detection.

In complex networks, identifying vital nodes is important to improve the reliability of the network and ensure its safe and effective operation. However, traditional centrality methods are one-sided and inaccurate, and centrality methods work differently in different networks. At once, many nodes have the same centrality value, making it difficult to distinguish differences between nodes. In order to better identify vital nodes and improve node resolution, inspired by graph structure learning, graph neural network was adopted to encode nodes and fit node influence, the propagation field learner was used to compute the propagation field of network nodes, and the degrees of node were observed as labels to train the model. Finally, the SI ( susceptibility-infection) model was used for propagation simulation, and kendall's correlation coefficient was used as a measure of node ordering monotonicity. The results in real world networks show that the algorithm has better better performance in measuring the sorting performance with the SI propagation model and the monotonicity of the sorting results with the kendall correlation coefficient, which improves the accuracy of vital nodes identification and the robustness of network node sorting. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell‐cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low‐dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial‐to‐mesenchymal transition and PI3K/AKT signaling in specific sub‐regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder‐based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.

Accurate traffic flow forecasting is vital for intelligent transportation systems, especially with urbanization worsening traffic congestion, which affects daily life, economic growth, and the environment. Precise forecasts aid in managing and optimizing transportation systems, reducing congestion, and improving air quality by cutting emissions. However, predicting outcomes is difficult due to intricate spatial relationships, nonlinear temporal patterns, and the challenges associated with long-term forecasting. Current research often uses static graph structures, overlooking dynamic and long-range dependencies. To tackle these issues, we introduce the spatiotemporal dynamic multi-hop network (ST-DMN), a Seq2Seq framework. This model incorporates spatiotemporal convolutional blocks (ST-Blocks) with residual connections in the encoder to condense historical traffic data into a fixed-dimensional vector. A dynamic graph represents time-varying inter-segment relationships, and multi-hop operation in the encoder's spatial convolutional layer and the decoder's diffusion multi-hop graph convolutional gated recurrent units (DMGCGRUs) capture long-range dependencies. Experiments on two real-world datasets METR-LA and PEMS-BAY show that ST-DMN surpasses existing models in three metrics. [ABSTRACT FROM AUTHOR]

Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation. [ABSTRACT FROM AUTHOR]

Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) current models broaden receptive fields by scaling the depth of GNNs, which is insufficient to preserve the semantics of long-range connectivity between distant but epidemic related areas. (2) Previous approaches model epidemics within single spatial scale, while ignoring the multi-scale epidemic patterns derived from different scales. To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph. Based on the learned multi-scale graph, we utilize a newly designed graph convolution module to exploit multi-scale epidemic patterns. This module allows us to facilitate multi-scale epidemic modeling by mining both scale-shared and scale-specific patterns. Experimental results on forecasting new cases of COVID-19 in United State demonstrate the superiority of our method over state-of-arts. Further analyses and visualization also show that MSGNN offers not only accurate, but also robust and interpretable forecasting result. Code is available at https://github.com/JashinKorone/MSGNN. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sparse structure learning in high‐dimensional Gaussian graphical models is an important problem in multivariate statistical inference, since the sparsity pattern naturally encodes the conditional independence relationship among variables. However, maximum a posteriori (MAP) estimation is challenging under hierarchical prior models, and traditional numerical optimization routines or expectation–maximization algorithms are difficult to implement. To this end, our contribution is a novel local linear approximation scheme that circumvents this issue using a very simple computational algorithm. Most importantly, the condition under which our algorithm is guaranteed to converge to the MAP estimate is explicitly stated and is shown to cover a broad class of completely monotone priors, including the graphical horseshoe. Further, the resulting MAP estimate is shown to be sparse and consistent in the ℓ2$$ {\ell}_2 $$‐norm. Numerical results validate the speed, scalability and statistical performance of the proposed method. [ABSTRACT FROM AUTHOR]

Accurately predicting Drug-Drug Interaction (DDI) is a critical and challenging aspect of the drug discovery process, particularly in preventing adverse reactions in patients undergoing combination therapy. However, current DDI prediction methods often overlook the interaction information between chemical substructures of drugs, focusing solely on the interaction information between drugs and failing to capture sufficient chemical substructure details. To address this limitation, we introduce a novel DDI prediction method: Multilayer Adaptive Soft Mask Graph Neural Network (MASMDDI). Specifically, we first design a multi-layer adaptive soft mask graph neural network to extract substructures from molecular graphs. Second, we employ an attention mechanism to mine substructure feature information and update latent features. In this process, to optimize the final feature representation, we decompose drug-drug interactions into pairwise interaction correlations between the core substructures of each drug. Third, we use these features to predict the interaction probabilities of DDI tuples and evaluate the model using real-world datasets. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods in DDI prediction. Furthermore, MASMDDI exhibits excellent performance in predicting DDIs of unknown drugs in two tasks that are more aligned with real-world scenarios. In particular, in the transductive scenario using the DrugBank dataset, the ACC and AUROC and AUPRC scores of MASMDDI are 0.9596, 0.9903, and 0.9894, which are 2% higher than the best performing baseline. [ABSTRACT FROM AUTHOR]

An accurate short-term load forecasting plays an important role in modern power system's operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods. [ABSTRACT FROM AUTHOR]

Cyber–physical systems (CPSs) serve as the pivotal core of Internet of Things (IoT) infrastructures, such as smart grids and intelligent transportation, deploying interconnected sensing devices to monitor operating status. With increasing decentralization, the surge in sensor devices expands the potential vulnerability to cyber attacks. It is imperative to conduct anomaly detection research on the multivariate time series data that these sensors produce to bolster the security of distributed CPSs. However, the high dimensionality, absence of anomaly labels in real-world datasets, and intricate non-linear relationships among sensors present considerable challenges in formulating effective anomaly detection algorithms. Recent deep-learning methods have achieved progress in the field of anomaly detection. Yet, many methods either rely on statistical models that struggle to capture non-linear relationships or use conventional deep learning models like CNN and LSTM, which do not explicitly learn inter-variable correlations. In this study, we propose a novel unsupervised anomaly detection method that integrates Sparse Autoencoder with Graph Transformer network (SGTrans). SGTrans leverages Sparse Autoencoder for the dimensionality reduction and reconstruction of high-dimensional time series, thus extracting meaningful hidden representations. Then, the multivariate time series are mapped into a graph structure. We introduce a multi-head attention mechanism from Transformer into graph structure learning, constructing a Graph Transformer network forecasting module. This module performs attentive information propagation between long-distance sensor nodes and explicitly models the complex temporal dependencies among them to enhance the prediction of future behaviors. Extensive experiments and evaluations on three publicly available real-world datasets demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In recent years, heterogeneous network/graph representation learning/embedding (HNE) has drawn tremendous attentions from research communities in multiple disciplines. HNE has shown its outstanding performances in various networked data analysis and mining tasks. In fact, most of real-world information networks in multiple fields can be modelled as the heterogeneous information networks (HIN). Thus, the HNE-based techniques can sufficiently capture rich-structured and semantic latent features from the given information network in order to facilitate for different task-driven learning tasks. This is considered as fundamental success of HNE-based approach in comparing with previous traditional homogeneous network/graph based embedding techniques. However, there are recent studies have also demonstrated that the heterogeneous network/graph modelling and embedding through graph neural network (GNN) is not usually reliable. This challenge is original come from the fact that most of real-world heterogeneous networks are considered as incomplete and normally contain a large number of feature noises. Therefore, multiple attempts have proposed recently to overcome this limitation. Within this approach, the meta-path-based heterogeneous graph-structured latent features and GNN-based parameters are jointly learnt and optimized during the embedding process. However, this integrated GNN and heterogeneous graph structure (HGS) learning approach still suffered a challenge of effectively parameterizing and fusing different graph-structured latent features from both GNN- and HGS-based sides into better task-driven friendly and noise-reduced embedding spaces. Therefore, in this paper we proposed a novel attention-supplemented heterogeneous graph structure embedding approach, called as: AGSE. Our proposed AGSE model supports to not only achieve the combined rich heterogeneous structural and GNN-based aggregated node representations but also transform achieved node embeddings into noise-reduced and task-driven friendly embedding space. Extensive experiments in benchmark heterogeneous networked datasets for node classification task showed the effectiveness of our proposed AGSE model in comparing with state-of-the-art network embedding baselines. [ABSTRACT FROM AUTHOR]

Accurately predicting the active power output of offshore wind power is of great significance for reducing the uncertainty in new power systems. By utilizing the spatiotemporal correlation characteristics among wind turbine unit outputs, this paper embeds the Diffusion Convolutional Neural Network (DCNN) into the Gated Recurrent Unit (GRU) for the feature extraction of spatiotemporal correlations in wind turbine unit outputs. It also combines graph structure learning to propose a sequence-to-sequence model for ultra-short-term power prediction in large offshore wind farms. Firstly, the electrical connection graph within the wind farm is used to preliminarily determine the reference adjacency matrix for the wind turbine units within the farm, injecting prior knowledge of the adjacency matrix into the model. Secondly, a convolutional neural network is utilized to convolve the historical curves of units within the farm along the time dimension, outputting a unit connection probability vector. The Gumbel–softmax reparameterization method is then used to make the probability vector differentiable, thereby generating an optimal adjacency matrix for the prediction task based on the probability vector. At the same time, the difference between the two adjacency matrices is added as a regularization term to the loss function to reduce model overfitting. The simulation of actual cases shows that the proposed model has good predictive performance in ultra-short-term power prediction for large offshore wind farms. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental condition. Its early and accurate diagnosis is critical in enhancing the quality of life for affected individuals. Graph neural networks supply promising approaches for ASD diagnosis. However, existing works typically focus on brain-level or population-level classification methods, where the former usually disregards subjects' non-imaging information and inter-subject relationships, and the latter generally fails to adequately evaluate and detect disease-associated brain regions and biomarkers. Furthermore, relatively static graph structures and shallow network architectures hinder the abundant extraction of information, affecting the performance of ASD identification. Accordingly, this paper proposes a new spectral graph network with brain-population graph structure joint learning (BPGLNet) for ASD diagnosis. This new framework involves two main components. Firstly, a brain-level graph learning module is designed to acquire valuable features of brain regions and identify effective biomarkers for each subject. In particular, it constructs a brain-aware representation learning network by fusing an improved graph pooling strategy and spectral graph convolution to learn subgraph structures and features of brain regions. Subsequently, based on these generated features, a population-level graph learning module is developed to capture relationships between different subjects. It builds an adaptive edge generator network by integrating non-imaging and imaging data, forming a learnable population graph. Further, this module also devises a deep cascade spectral graph network to enrich high-level feature representation of data and complete ASD identification. Experiments on the benchmark dataset reveal the state-of-the-art performance of BPGLNet. [ABSTRACT FROM AUTHOR]

• We propose an automated end-to-end graph structure learning model, GSL-DTI, for Drug-Target Interaction (DTI) prediction. In contrast to previous studies relying on manual rules, our approach incorporates an automatic graph structure learning method, which utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. • We conduct experiments on three public datasets and compare our method against competitive baselines. The experimental results demonstrate a significant outperformance of our model over state-of-the-art methods. • Furthermore, the introduction of graph structure learning offers a fresh perspective for DTI prediction research. To the best of our knowledge, GSL-DTI represents the first attempt to apply automatic graph structure learning to DTI tasks. It reduces the reliance on expert knowledge and yields improved node representations for downstream tasks. Motivation: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta -paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction. [ABSTRACT FROM AUTHOR]

Graph-based fraud detection methods have recently attracted much attention due to the rich relational information of graph-structured data, which may facilitate the detection of fraudsters. However, the GNN-based algorithms may exhibit unsatisfactory performance faced with graph heterophily as the fraudsters usually disguise themselves by deliberately making extensive connections to normal users. In addition to this, the class imbalance problem also causes GNNs to overfit normal users and perform poorly for fraudsters. To address these problems, we propose an Imbalanced Graph Structure Learning framework for fraud detection (IGSL for short). Specifically, nodes are picked with a devised multi-relational class-balanced sampler for mini-batch training. Then, an iterative graph structure learning module is proposed to iteratively construct a global homophilic adjacency matrix in the embedding domain. Further, an anchor node message passing mechanism is proposed to reduce the computational complexity of the constructing homophily adjacency matrix. Extensive experiments on benchmark datasets show that IGSL achieves significantly better performance even when the graph is heavily heterophilic and imbalanced. [ABSTRACT FROM AUTHOR]

Traffic speed forecasting is a crucial aspect of traffic management that requires an accurate multi spatiotemporal time series forecasting technique. Previous studies typically employ graph neural network (GNN)-based methods for this task, but they are limited by their focus on spatial dependence based on real geographic distance in road networks. These structures are often inadequate for accurately describing spatial dependencies in the real world. Recently, multigraph neural networks (MGNNs) have shown considerable promise for improving forecasting performance by modelling graph structures from different spatial relationships. However, these kinds of methods do not account for complex relationships between aspects and latent dependence that cannot be known beforehand. To address these shortcomings, we propose a novel traffic speed forecasting method called adaptive multigraph convolutional networks (AMGCN), where we first introduce five predefined graphs based on spatial distance, accessibility, pattern similarity, distribution similarity and KL divergence. We fuse these graphs into a complex prior graph using a method based on spatial attention and graph relation attention. In this process, the spatial dependence in the road network is modelled comprehensively from multiple perspectives. In addition, we introduce the adaptive graph to calculate the similarity between learnable node embeddings to assist the forecasting. In this process, spatial dependencies that still cannot be captured by predefined graphs can be obtained by the way of data-driven. We utilize a mix-hop graph convolution with a residual connection to capture spatial dependencies in prior graphs and adaptive graphs. Time dependencies are also captured through causal convolution based on equidistance downsampling to prevent overfitting and redundancy in capturing spatiotemporal interactions. Extensive experiments on four real-world datasets demonstrate that our proposed method achieves superior performance compared to other baselines and effectively captures the spatiotemporal dependencies of the road network. Source codes are available at https://github.com/hfimmortal/AMGCN. [ABSTRACT FROM AUTHOR]

A smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving unwrinkled traffic flow. However, due to its spatial and temporal interdependence, traffic forecasting might be more difficult. Graph neural networks (GNN) are widely used to obtain traffic forecasting due to their capability to operate in non-Euclidean data. The topological connection of the traffic network plays a crucial role in graph structure learning. Therefore, the importance of the adjacency matrix in graph construction might be significant for effective traffic forecasting. As a result, for efficient graph construction, a Deep Spatial-Temporal Graph Neural Network (DSTGNN) is proposed for the construction of the weighted adjacency matrix and traffic speed prediction accurately by recording topological and temporal information. Three different weighted adjacency matrices are suggested depending on three different proposed algorithms for different variations of traffic's spatial condition. The new adjacency matrix is used in a novel DSTGNN for predicting the traffic state. The novel model comprised multiple layers with skip connections to adequately extract the variation of temporal and spatial information. DSTGNN outperforms the standard and other graph neural network models on four traffic speed datasets, SZ-taxi, Los-loop, PeMSD7, and METR-LA. [ABSTRACT FROM AUTHOR]

Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns. However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection. It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.

Multispectral point cloud, with spatial and multiple-band spectral information, provides the data basis for finer land cover 3-D classification. However, spectral information is not well utilized by traditional methods of point cloud classification. Benefiting from the excellent performance of graph neural networks on non-Euclidean data, it is well suited to the joint use of spatial and spectral information from multispectral point clouds to achieve better classification performance. However, existing graph-based methods for point cloud classification rely on manual experience to construct input graph and cannot adapt to the complexity of remote sensing scenes. In this article, we propose a novel multikernel graph structure learning (MKGSL) framework for multispectral point cloud classification. Specifically, we explore the high-dimensional feature distribution properties of multispectral point clouds in Hilbert space through the use of kernel method. An innovative multiple-kernel learning mechanism is embedded into our network, which allows to obtain better mappings adaptively. Simultaneously, a series of prior constraints designed based on land cover distribution characteristics are imposed on the network training process, which leads the learned graph of the multispectral point cloud to facilitate better classification. Our method is dedicated to adaptively constructing task-oriented graph structures to improve the performance of multispectral point cloud classification. Experimental comparisons demonstrate that the proposed MKGSL performs better than several state-of-the-art methods on two real multispectral point cloud datasets.

Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. However, a key limitation of these models is that the inputs, namely brain networks/graphs, are constructed using predefined statistical metrics (e.g., Pearson correlation) and are not learnable. The lack of learnability restricts the flexibility of these approaches. While statistically-specific brain networks can be highly effective in recognizing certain diseases, their performance may not exhibit robustness when applied to other types of brain disorders. To address this issue, we propose a novel module called Brain Structure Inference (termed BSI), which can be seamlessly integrated with multiple downstream tasks within a unified framework, enabling end-to-end training. It is highly flexible to learn the most beneficial underlying graph structures directly for specific downstream tasks. The proposed method achieves classification accuracies of 74.83% and 79.18% on two publicly available datasets, respectively. This suggests an improvement of at least 3% over the best-performing existing methods for both tasks. In addition to its excellent performance, the proposed method is highly interpretable, and the results are generally consistent with previous findings.

Personalized news recommendation is an important technology to help users obtain the news information they are interested in and alleviate information overload. In recent years, with the development of information technology and society, personalized news recommendation has been increasingly widely studied, and has achieved remarkable success in improving the news reading experience of users. This paper aims to systematically summarize personalized news recommendation methods based on deep learning. Firstly, it introduces personalized news recommendation methods and analyzes their characteristics and influencing factors. Then, the overall framework of personalized news recommendation is given, and the personalized news recommendation methods based on deep learning are analyzed and summarized. On this basis, it focuses on personalized news recommendation methods based on graph structure learning, including user-news interaction graph, knowledge graph and social relationship graph. Finally, it analyzes the challenges of the current personalized news recommendation, discusses how to solve the problems of data sparsity, model interpretability, diversity of recommendation results and news privacy protection in personalized news recommendation system, and puts forward more specific and operable research ideas in the future research direction.

Abstract An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.