Graph Convolutional Network Model Research Articles

Basketball Sports Posture Recognition Technology Based on Improved Graph Convolutional Neural Network

Basketball has rapidly developed in recent years. Analysis of various moves in basketball can provide technical references for professional players and assist referees in judging games. Traditional technology can no longer provide modern basketball players with theoretical support. Therefore, using intelligent methods to recognize human body postures in basketball was a relatively innovative approach. To be able to recognize the basketball sports posture of players more accurately, the experiment proposes a basketball stance recognition model based on enhanced graph convolutional networks (GCN), that is, the basketball stance recognition model based on enhanced GCN and spatial temporal graph convolutional network (ST-GCN) model. This model combines the respective advantages of the GCN and temporal convolutional network and can handle graph-structured data with time-series relationships. The ST-GCN can be further deduced by realizing the convolution operation of the graph structure and establishing a spatiotemporal graph convolution model for the posture sequence of a person’s body. A dataset of technical basketball actions is constructed to verify the effectiveness of the ST-GCN model. The final experimental findings indicated that the final recognition accuracy of the ST-GCN model for basketball postures was approximately 95.58%, whereas the final recognition accuracy of the long short term memory + multiview re-observation skeleton action recognition (LSTM+MV+AC) model was about 93.65%.

A self-attention dynamic graph convolution network model for traffic flow prediction

A self-attention dynamic graph convolution network model for traffic flow prediction

A knowledge-driven graph convolutional network for abnormal electrocardiogram diagnosis

The electrocardiogram (ECG) signal comprising P-, Q-, R-, S-, and T-waves is an indispensable noninvasive diagnostic tool for analyzing physiological conditions of the heart. In general, traditional ECG intelligent diagnosis methods gradually extract features of the signal from input data until they can classify the ECG signal. However, the decision-making process of ECG intelligence models is implicit to clinicians. Clinical experts rely on clear and specific features extracted from ECG data to diagnose cardiac diseases effectively. Inspired by this clinical diagnosis mechanism, we propose an ECG knowledge graph (ECG-KG) framework primarily to improve ECG classification by presenting knowledge of ECG clinical diagnosis. In particular, the ECG-KG framework contains an ECG semantic feature extraction module, a knowledge graph construction module, and an ECG classification module. First, the ECG semantic feature extraction module locates the key points using the difference value method and further calculates the ECG attribute features. Further, the knowledge graph construction module utilizes attribute features to design entities and relationships for constructing abnormal ECG triples. The triples vectorize ECG abnormalities through the strategy of knowledge graph embedding strategy. Finally, the ECG classification module combines the ECG knowledge graph with the graph convolutional network model and adequately integrates expert knowledge to identify ECG abnormalities. Experiments conducted on the benchmark QT, the CPSC-2018, and the ZZU-ECG datasets show that the ECG-KG framework considerably outperforms other ECG diagnosis models, indicating the effectiveness of the ECG-KG framework for ECG abnormality diagnosis.

Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.

Learning Aligned Vertex Convolutional Networks for Graph Classification.

Graph convolutional networks (GCNs) are powerful tools for graph structure data analysis. One main drawback arising in most existing GCN models is that of the oversmoothing problem, i.e., the vertex features abstracted from the existing graph convolution operation have previously tended to be indistinguishable if the GCN model has many convolutional layers (e.g., more than two layers). To address this problem, in this article, we propose a family of aligned vertex convolutional network (AVCN) models that focus on learning multiscale features from local-level vertices for graph classification. This is done by adopting a transitive vertex alignment algorithm to transform arbitrary-sized graphs into fixed-size grid structures. Furthermore, we define a new aligned vertex convolution operation that can effectively learn multiscale vertex characteristics by gradually aggregating local-level neighboring aligned vertices residing on the original grid structures into a new packed aligned vertex. With the new vertex convolution operation to hand, we propose two architectures for the AVCN models to extract different hierarchical multiscale vertex feature representations for graph classification. We show that the proposed models can avoid iteratively propagating redundant information between specific neighboring vertices, restricting the notorious oversmoothing problem arising in most spatial-based GCN models. Experimental evaluations on benchmark datasets demonstrate the effectiveness.

ScaleGCN: Efficient and Effective Graph Convolution via Channel-Wise Scale Transformation.

Graph convolutional networks (GCNs) have shown success in many graph-based applications as they can combine node features and graph topology to obtain expressive embeddings. While there exist numerous GCN variants, a typical graph convolution layer uses neighborhood aggregation and fully-connected (FC) layers to extract topological and node-wise features, respectively. However, when the receptive field of GCNs becomes larger, the tight coupling between the number of neighborhood aggregation and FC layers can increase the risk of over-fitting. Also, the FC layer between two successive aggregation operations will mix and pollute features in different channels, bringing noise and making node features hard to converge at each channel. In this article, we explore graph convolution without FC layers. We propose scale graph convolution, a new graph convolution using channel-wise scale transformation to extract node features. We provide empirical evidence that our new method has lower over-fitting risk and needs fewer layers to converge. We show from both theoretical and empirical perspectives that models with scale graph convolution have lower computational and memory costs than traditional GCN models. Experimental results on various datasets show that our method can achieve state-of-the-art results, in a cost-effective fashion.

Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram.

Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927). The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. GCN based on MRI could predict MVI on HCC.

Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.

Abstract 4945: Computational modeling-based discovery of novel anticancer drug candidates targeting centromere-associated protein E

Abstract Purpose: Centromere-associated protein E (CENP-E), a mitotic motor protein, is crucial for cell division. Inhibition of CENP-E can lead to chromosome misalignment and aneuploidy. Our recent studies revealed that the CENP-E inhibitor (CENP-Ei), Compound A, induces aneuploid-mediated cGAS-STING pathway activation in cancer cell lines, which is expected to trigger innate immune response and induce immunological conversion of the tumor microenvironment from cold to hot. However, known CENP-Eis exhibited limited efficacy, and none have gone beyond phase I trials since 2012. The aim of this study is to develop a workflow integrating structure and artificial intelligence-based modeling approaches to accelerate the discovery of novel CENP-Eis with improved efficacy and overcome related challenges. Method: The inhibitor-bound CENP-E structure was modeled using chimeric homology modeling followed by induced fit docking with known CENP-Eis. Structure-based virtual screening (VS) was performed on large compound libraries in Enamine REAL, Synthetically Accessible Virtual Inventory, and MolPort databases. Consensus scoring methods were applied by averaging ranks of individual molecules obtained from VS against different CENP-E conformations. An active learning-enhanced VS pipeline was developed using ATOM Modeling PipeLine (AMPL) to find the best-ranked ligands more efficiently. CENP-E ligand candidates were further filtered based on their safety properties predicted by machine learning models trained with curated databases of adverse drug reactions using AMPL. The success of this study was measured by its ability to reveal novel chemotypes that could modulate CENP-E in kinesin ATPase assays. Result: Conformations of CENP-E’s ligand binding site were predicted for VS using the homology model. Structure-based VS was performed to select high-ranked compounds based on consensus scoring. The active learning-enhanced VS pipeline achieved 75% accuracy in ranking the top 20% of hit compounds. The graph convolutional network model with a ROCAUC, 0.76, was created using AMPL to filter ligand candidates based on their predicted safety properties. Seventy-three potential CENP-E ligands computationally prioritized from the Molport database were experimentally tested using kinesin ATPase assays from which 4 potential inhibitors and 8 potential activators were identified indicating overlapping binding sites of inhibitors and activators. Conclusion: Our integrated workflow is effective in discovering novel CENP-E modulators. The discovery of CENP-E activators provides an opportunity to identify novel therapeutics in hepatocellular carcinoma malignancies known to experience decreased CENP-E activity leading to aneuploidy. The predictions generated by computational models combined with experimental validations could further accelerate anticancer drug discovery. Citation Format: Pinyi Lu, Ryo Kamata, Michael R. Weil, Naomi Ohashi, Akihiro Ohashi, Eric A. Stahlberg. Computational modeling-based discovery of novel anticancer drug candidates targeting centromere-associated protein E [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4945.

AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition

The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a special emphasis on pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) and long short-term memory (LSTM) neural networks have shown promise in predicting air quality trends in time series data. However, challenges persist due to the unpredictability of air quality data and the scarcity of long-term historical data for training. To address these challenges, this study introduces the AIoT-enhanced EEMD-CEEMDAN-GCN model. This innovative approach involves decomposing the input signal using EEMD (Ensemble Empirical Mode Decomposition) and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to extract intrinsic mode functions. These functions are then processed through a GCN (Graph Convolutional Network) model, enabling precise prediction of air quality trends. The model’s effectiveness is validated using air pollution datasets from four provinces in China, demonstrating its superiority over various deep learning models (GCN, EMD-GCN) and series decomposition models (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy and better data fitting, outperforming other models in key metrics such as MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and R2 (Coefficient of Determination). The implementation of this AIoT-enhanced model in air pollution prediction allows decision-makers to more accurately anticipate changes in air quality, particularly concerning carbon emissions. This facilitates more effective planning of mitigation measures, improvement of public health, and optimization of resource allocation. Moreover, the model adeptly addresses the complexities of air quality data, contributing significantly to enhanced monitoring and management strategies in the context of sustainable urban development and environmental conservation.

Enhancing predictive models for illicit activities in the Bitcoin transaction network using advanced graph analytical techniques

The Elliptic dataset compiles a comprehensive history of Bitcoin transactions, integrating both anti-money laundering (AML) tags and distinct graph network features. Given the nature of the Bitcoin transaction networka complex, weakly interconnected structureleveraging graph analysis techniques for its study holds immense potential, especially in the realm of detecting illicit activities like hacking, drug trades, gambling, and more. A detailed examination of the Elliptic dataset, encompassing transaction amounts, frequencies, source and destination addresses, sheds light on the inherent structure and peculiarities of the Bitcoin transaction ecosystem. By conceptualizing this transactional landscape as a graph, a slew of analytical attributes emerge: node degree distribution, community architecture, centrality measures, and so forth. Such attributes pave the way for the creation of predictive models that can pinpoint and prognosticate potential unlawful trade actions. Several computational models have been employed on the Elliptic dataset, such as Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN). The authors of this particular study delve into augmentations of the GCN model, juxtaposing the efficacy of the original GCN model against their enhanced algorithm within the context of the Elliptic dataset.

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation.

The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling the identification of new therapeutic applications for existing drugs. In the present study, a trained deep-learning model was employed to screen a library of FDA-approved drugs to discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active and decoy compounds specific to JAK2 were obtained from the DUD-E database. RDKit, a cheminformatic toolkit, was utilized to extract molecular features from the compounds. The DeepChem framework's GraphConvMol, based on graph convolutional network models, was applied to build a predictive model using the DUD-E datasets. Subsequently, the trained deep-learning model was used to predict the JAK2 inhibitory potential of FDA-approved drugs. Based on these predictions, ribociclib, topiroxostat, amodiaquine, and gefitinib were identified as potential JAK2 inhibitors. Notably, several known JAK2 inhibitors demonstrated high potential according to the prediction results, validating the reliability of our prediction model. To further validate these findings and confirm their JAK2 inhibitory activity, molecular docking experiments were conducted using tofacitinib-an FDA-approved drug for JAK2 inhibition. Experimental validation successfully confirmed our computational analysis results by demonstrating that these novel drugs exhibited comparable inhibitory activity against JAK2 compared to tofacitinib. In conclusion, our study highlights how deep learning models can significantly enhance virtual screening efforts in drug discovery by efficiently identifying potential candidates for specific targets such as JAK2. These newly discovered drugs hold promises as novel JAK2 inhibitors deserving further exploration and investigation.

Spatial-Pooling-Based Graph Attention U-Net for Hyperspectral Image Classification

In recent years, graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image (HSI) classification owing to their exceptional representation capabilities. However, the high computational requirements of GCNs have led most existing GCN-based HSI classification methods to utilize superpixels as graph nodes, thereby limiting the spatial topology scale and neglecting pixel-level spectral–spatial features. To address these limitations, we propose a novel HSI classification network based on graph convolution called the spatial-pooling-based graph attention U-net (SPGAU). Specifically, unlike existing GCN models that rely on fixed graphs, our model involves a spatial pooling method that emulates the region-growing process of superpixels and constructs multi-level graphs by progressively merging adjacent graph nodes. Inspired by the CNN classification framework U-net, SPGAU’s model has a U-shaped structure, realizing multi-scale feature extraction from coarse to fine and gradually fusing features from different graph levels. Additionally, the proposed graph attention convolution method adaptively aggregates adjacency information, thereby further enhancing feature extraction efficiency. Moreover, a 1D-CNN is established to extract pixel-level features, striking an optimal balance between enhancing the feature quality and reducing the computational burden. Experimental results on three representative benchmark datasets demonstrate that the proposed SPGAU outperforms other mainstream models both qualitatively and quantitatively.

Multiscale Feature Fusion and Graph Convolutional Network for Detecting Ethereum Phishing Scams

With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models.

GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting

Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to meet real-time prediction requirements. (2) Feature extraction is a crucial step in photovoltaic power generation prediction. However, traditional feature extraction methods often focus solely on surface features, and fail to capture the inherent relationships between various influencing factors in photovoltaic power generation data, such as light intensity, temperature, and more. To overcome these limitations, this paper proposes a mid-term PV power prediction model that combines Graph Convolutional Network (GCN) and Informer models. This fusion model leverages the multi-output capability of the Informer model to ensure the timely generation of predictions for long sequences. Additionally, it harnesses the feature extraction ability of the GCN model from nodes, utilizing graph convolutional modules to extract feature information from the ‘query’ and ‘key’ components within the attention mechanism. This approach provides more reliable feature information for mid-term PV power prediction, thereby ensuring the accuracy of long sequence predictions. Results demonstrate that the GCN–Informer model significantly reduces prediction errors while improving the precision of power generation forecasting compared to the original Informer model. Overall, this research enhances the prediction accuracy of PV power generation and contributes to advancing the field of clean energy.

Identification of breast cancer subtypes based on graph convolutional network

Identification of molecular subtypes of malignant tumors plays a vital role in individualized diagnosis, personalized treatment, and prognosis prediction of cancer patients. The continuous improvement of comprehensive tumor genomics database and the ongoing breakthroughs in deep learning technology have driven further advancements in computer-aided tumor classification. Although the existing classification methods based on gene expression omnibus database take the complexity of cancer molecular classification into account, they ignore the internal correlation and synergism of genes. To solve this problem, we propose a multi-layer graph convolutional network model for breast cancer subtype classification combined with hierarchical attention network. This model constructs the graph embedding datasets of patients' genes, and develops a new end-to-end multi-classification model, which can effectively recognize molecular subtypes of breast cancer. A large number of test data prove the good performance of this new model in the classification of breast cancer subtypes. Compared to the original graph convolutional neural networks and two mainstream graph neural network classification algorithms, the new model has remarkable advantages. The accuracy, weight-F1-score, weight-recall, and weight-precision of our model in seven-category classification has reached 0.851 7, 0.823 5, 0.851 7 and 0.793 6 respectively. In the four-category classification, the results are 0.928 5, 0.894 9, 0.928 5 and 0.865 0 respectively. In addition, compared with the latest breast cancer subtype classification algorithms, the method proposed in this paper also achieved the highest classification accuracy. In summary, the model proposed in this paper may serve as an auxiliary diagnostic technology, providing a reliable option for precise classification of breast cancer subtypes in the future and laying the theoretical foundation for computer-aided tumor classification.

Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction

Accurate and fine-grained traffic state prediction has always been an important research field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature evolution patterns are deeply recessive, posing challenges in effectively characterizing and modeling them. This paper proposed a novel spatial–temporal graph convolution network model with traffic Fundamental Diagram (FD) information informed. The model decouples the high-dimensional spatiotemporal relationships in the transportation network and fully leverages the physical evolution laws of traffic states. First, the Graph Convolutional Network (GCN) with a spatial attention mechanism was proposed to capture spatial relations of the road network. The mechanism can better represent the spatial dynamics of the graph adjacency matrix in GCN. Second, this study injected prior physical knowledge into the graph adjacency matrix. This process was achieved by embedding characteristics of FDs from historical traffic data on the diagonal of the matrix, by which the propagation pattern of traffic states in road network could be considered. Third, to further catch the time dependence of the road network, the Gated Recurrent Unit (GRU) structure and the Transformer encoding structure were employed to locally and globally reform traffic state time sequences. Finally, experiments on a revised traffic dataset demonstrated that the proposed method consistently outperforms other baselines regarding Mean Absolute Error and Root Mean Square Error across all cases. Moreover, it achieved the optimal Mean Absolute Percentage Error in the 30- and 60-minute prediction tasks. This study shows a novel solution to inform traffic physical laws into data-driven state prediction models, and the reliability of the proposed method in long-term prediction offers valuable support for improving traffic management and alleviating traffic congestion.

An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model

Accurate and robust wind power forecasting (WPF) is great significance to ensure the safe and stable operation of the power system and promote the transformation of low-carbon energy. However, the high randomness and intermittency of wind power bring great challenges when designing reliable forecasting models. In this paper, a novel spatial-temporal attention graph convolutional network model is proposed. Firstly, the spatial attention mechanism is used to aggregate and extract the spatial correlations of the raw wind power data. Secondly, the temporal attention mechanism is applied to capture the temporal correlations. Then, the extracted spatial-temporal correlations were put into the temporal convolution network and the spatial convolution network to further obtain the temporal and spatial dependencies. Finally, the wind power forecasting results is output through the full connection layer. The proposed method is verified by using wind power data from real wind farm in China. The experimental results reveal that the proposed depth spatiotemporal prediction model has more significant advantages than other advanced models in terms of prediction accuracy and stability.

SEGCN: a subgraph encoding based graph convolutional network model for social bot detection

Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler–Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively.

ScMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.