Node Embedding Research Articles

Identifying the factors influencing the development of bilateral investment treaties with health safeguards: a Machine Learning-based link prediction approach

A network analysis approach, complemented by machine learning (ML) techniques, is applied to analyse the factors influencing Bilateral Investment Treaties (BITs) at the country level. Using the Electronic Database of Investment Treaties, BITs with health safeguards from 167 countries were charted, resulting in 534 connections with countries as nodes and their BITs as edges. Network analysis found that, on average, a country established BITs with six other nations. Additionally, we used node embedding techniques to generate features from the network, such as the Jaccard coefficient, resource allocation, and Adamic Adar for downstream link prediction. This study employed five tree-based ML models to predict future BIT formations with health inclusion. The eXtreme Gradient Boosting model proved to be superior, achieving a 64.02% accuracy rate. Notably, the Common Neighbor centrality feature and the Capital Account Balance Ratio emerged as influential factors in creating new BITs with health inclusions. Beyond economic considerations, our study highlighted a vital intersection: the nexus between BITs, economic growth, and public health policies. In essence, this research underscores the importance of safeguarding public health in BITs and showcases the potential of ML in understanding the intricacies of international treaties.

NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction

Predicting ncRNA–protein interactions (NPIs) is essential for understanding regulatory roles in cellular processes and disease mechanisms, yet experimental methods are costly and time-consuming. In this study, we propose NPI-WGNN, a novel weighted graph neural network model designed to enhance NPI prediction by incorporating topological insights from graph structures. Our approach introduces a bipartite version of the high-order common neighbor (HOCN) similarity metric to assign edge weights in an ncRNA–protein network, refining node embeddings via weighted node2vec. We further enrich these embeddings with centrality measures, such as degree and Katz centralities, to capture network hierarchy and connectivity. To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. An ablation study confirms the complementary strengths of these layers, showing that their integration improves predictive accuracy and robustness across varied graph complexities. Experimental results on three benchmark datasets demonstrate that NPI-WGNN outperforms state-of-the-art methods, achieving up to 96.1% accuracy, 97.5% sensitivity, and an F1-score of 0.96, positioning it as a robust and accurate framework for ncRNA–protein interaction prediction.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

IGCN: A Provably Informative GCN Embedding for Semi-Supervised Learning With Extremely Limited Labels.

Graph Neural Networks (GNNs) have gained much more attention in the representation learning for the graph-structured data. However, the labels are always limited in the graph, which easily leads to the overfitting problem and causes the poor performance. To solve this problem, we propose a new framework called IGCN, short for Informative Graph Convolutional Network, where the objective of IGCN is designed to obtain the informative embeddings via discarding the task-irrelevant information of the graph data based on the mutual information. As the mutual information for irregular data is intractable to compute, our framework is optimized via a surrogate objective, where two terms are derived to approximate the original objective. For the former term, it demonstrates that the mutual information between the learned embeddings and the ground truth should be high, where we utilize the semi-supervised classification loss and the prototype based supervised contrastive learning loss for optimizing it. For the latter term, it requires that the mutual information between the learned node embeddings and the initial embeddings should be high and we propose to minimize the reconstruction loss between them to achieve the goal of maximizing the latter term from the feature level and the layer level, which contains the graph encoder-decoder module and a novel architecture GCN Info. Moreover, we provably show that the designed GCN Info can better alleviate the information loss and preserve as much useful information of the initial embeddings as possible. Experimental results show that the IGCN outperforms the state-of-the-art methods on 7 popular datasets.

Robust graph mutual-assistance convolutional networks for semi-supervised node classification tasks

Graph convolutional networks (GCNs) have made substantial progress in semi-supervised learning. However, GCNs are sensitive to graph noise, making the reconstruction of imperfect graph structures essential for adapting GCNs to complex scenarios. Prevalent GCNs employ graph augmentation to generate reliable augmented graphs, thereby mining potential graph information. Since traditional GCN layers can only perform message passing independently within each graph, GCNs primarily concentrate on fusing information from the node embeddings independently learned by the reliable augmented graphs. Nevertheless, the neglect of cross-graph information exchange during message passing limits the model's inter-graph interactions ability. In this paper, we propose a GCN framework called Robust graph mutual-assistance convolutional networks (RamNet), which introduces cross-graph information exchange mechanisms into the GCN layers. First, RamNet employs a multi-metric topology augmentation strategy to generate a synthesized graph, thereby exploring more reliable information. Second, it learns inter-graph interaction information by achieving information exchange between the raw graph and the synthesized graph during message passing. Finally, in addition to utilizing a small amount of supervised information, it also captures rich self-supervision information by maximizing consistency relationship between input graphs. We conduct comprehensive experiments in both standard and noisy scenarios, highlighting the superior performance of RamNet.

MCoGCN-motif high-order feature-guided embedding learning framework for social link prediction

Traditional social link prediction models primarily concentrate on the adjacency features of the network, overlooking the rich high-order structural information within. Therefore, the study of effective extraction and encoding of these high-order features, and their integration into prediction models, holds significant theoretical and practical value. To address this challenge, we propose a novel embedding learning framework guided by motif high-order features for social link prediction tasks. Firstly, we utilize the motif adjacency matrix to capture complex patterns in social networks. Through a propagation process, node embeddings can carry the structural information of the network. Subsequently, we design a simplified attention mechanism, allowing embeddings carrying motif high-order features to guide the representation of embeddings based on adjacency features. We then employ a feed-forward neural network to optimize node embeddings. Specifically, this framework addresses the issue of weakly correlated nodes in the network, which struggle to learn effective embeddings due to a lack of direct information. By guiding with high-order motif features, the framework enhances the similarity and predictive power of these node embeddings. Finally, we conducted a detailed evaluation of the predictive performance of our model on four social networks. The experimental results indicate that our model exhibits high accuracy and advantages in predicting social links.

Multi-angle information aggregation for inductive temporal graph embedding.

Graph embedding has gained significant popularity due to its ability to represent large-scale graph data by mapping nodes to a low-dimensional space. However, most of the existing research in this field has focused on transductive learning, where fixed node embeddings are generated by training the entire graph. This approach is not well-suited for temporal graphs that undergo continuous changes with the addition of new nodes and interactions. To address this limitation, we propose an inductive temporal graph embedding method called MIAN (Multi-angle Information Aggregation Network). The key focus of MIAN is to design an aggregation function that combines multi-angle information for generating node embeddings. Specifically, we divide the information into different angles, including neighborhood, temporal, and environment. Each angle of information is modeled and mined independently, and then fed into an improved gated recuttent unit (GRU) module to effectively combine them. To assess the performance of MIAN, we conduct extensive experiments on various real-world datasets and compare its results with several state-of-the-art baseline methods across diverse tasks. The experimental findings demonstrate that MIAN outperforms these methods.

Fusing multiplex heterogeneous networks using graph attention-aware fusion networks

Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type. However, a large number of real-world networks include multiple types of nodes and edges. Enabling these architectures to work on networks with multiple node and edge types brings additional challenges due to the heterogeneity of the networks and the multiplicity of the existing associations. In this study, we present a framework, named GRAF (Graph Attention-aware Fusion Networks), to convert multiplex heterogeneous networks to homogeneous networks to make them more suitable for graph representation learning. Using attention-based neighborhood aggregation, GRAF learns the importance of each neighbor per node (called node-level attention) followed by the importance of each network layer (called network layer-level attention). Then, GRAF processes a network fusion step weighing each edge according to the learned attentions. After an edge elimination step based on edge weights, GRAF utilizes Graph Convolutional Networks (GCN) on the fused network and incorporates node features on graph-structured data for a node classification or a similar downstream task. To demonstrate GRAF’s generalizability, we applied it to four datasets from different domains and observed that GRAF outperformed or was on par with the baselines and state-of-the-art (SOTA) methods. We were able to interpret GRAF’s findings utilizing the attention weights. Source code for GRAF is publicly available at https://github.com/bozdaglab/GRAF.

Deep multiple instance learning on heterogeneous graph for drug–disease association prediction

Drug repositioning offers promising prospects for accelerating drug discovery by identifying potential drug–disease associations (DDAs) for existing drugs and diseases. Previous methods have generated meta-path-augmented node or graph embeddings for DDA prediction in drug–disease heterogeneous networks. However, these approaches rarely develop end-to-end frameworks for path instance-level representation learning as well as the further feature selection and aggregation. By leveraging the abundant topological information in path instances, more fine-grained and interpretable predictions can be achieved. To this end, we introduce deep multiple instance learning into drug repositioning by proposing a novel method called MilGNet. MilGNet employs a heterogeneous graph neural network (HGNN)-based encoder to learn drug and disease node embeddings. Treating each drug–disease pair as a bag, we designed a special quadruplet meta-path form and implemented a pseudo meta-path generator in MilGNet to obtain multiple meta-path instances based on network topology. Additionally, a bidirectional instance encoder enhances the representation of meta-path instances. Finally, MilGNet utilizes a multi-scale interpretable predictor to aggregate bag embeddings with an attention mechanism, providing predictions at both the bag and instance levels for accurate and explainable predictions. Comprehensive experiments on five benchmarks demonstrate that MilGNet significantly outperforms ten advanced methods. Notably, three case studies on one drug (Methotrexate) and two diseases (Renal Failure and Mismatch Repair Cancer Syndrome) highlight MilGNet’s potential for discovering new indications, therapies, and generating rational meta-path instances to investigate possible treatment mechanisms. The source code is available at https://github.com/gu-yaowen/MilGNet.

ActiveReach: an active learning framework for approximate reachability query answering in large-scale graphs.

With graph reachability query, one can answer whether there exists a path between two query vertices in a given graph. The existing reachability query processing solutions use traditional reachability index structures and can only compute exact answers, which may take a long time to resolve in large graphs. In contrast, with an approximate reachability query, one can offer a compromise by enabling users to strike a trade-off between query time and the accuracy of the query result. In this study, we propose a framework, dubbed ActiveReach, for learning index structures to answer approximate reachability query. ActiveReach is a two-phase framework that focuses on embedding nodes in a reachability space. In the first phase, we leverage node attributes and positional information to create reachability-aware embeddings for each node. These embeddings are then used as nodes' attributes in the second phase. In the second phase, we incorporate the new attributes and include reachability information as labels in the training data to generate embeddings in a reachability space. In addition, computing reachability for all training data may not be practical. Therefore, selecting a subset of data to compute reachability effectively and enhance reachability prediction performance is challenging. ActiveReach addresses this challenge by employing an active learning approach in the second phase to selectively compute reachability for a subset of node pairs, thus learning the approximate reachability for the entire graph. Our extensive experimental study with various real attributed large-scale graphs demonstrates the effectiveness of each component of our framework.

Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge

Influence Maximization (IM) stands as a central challenge within the domain of complex network analysis, with the primary objective of identifying an optimal seed set of a predetermined size that maximizes the reach of influence propagation. Over time, numerous methodologies have been proposed to address the IM problem. However, one certain network referred to as Imbalanced Heterogeneous Networks (IHN), which widely used in social situation, urban and rural areas, and merchandising, presents challenges in achieving high-quality solutions. In this work, we introduce the Lightweight Reinforcement Learning algorithm with Prior knowledge (LRLP), which leverages the Struc2Vec graph embedding technique that captures the structural similarity of nodes to generate vector representations for nodes within the network. In details, LRLP incorporates prior knowledge based on a group of centralities, into the initial experience pool, which accelerates the reinforcement learning training for better solutions. Additionally, the node embedding vectors are input into a Deep Q Network (DQN) to commence the lightweight training process. Experimental evaluations conducted on synthetic and real networks showcase the effectiveness of the LRLP algorithm. Notably, the improvement seems to be more pronounced when the the scale of the network is larger. We also analyze the effect of different graph embedding algorithms and prior knowledge on algorithmic results. Moreover, we conduct an analysis about some parameters, such as number of seed set selections T, embedding dimension d and network update frequency C. It is significant that the reduction of number of seed set selections T not only keeps the quality of solutions, but lowers the algorithm’s computational cost.

Network embedding on metric of relation

Network embedding maps the nodes of a given network into a low-dimensional space such that the semantic similarities among the nodes can be effectively inferred. Most existing approaches use inner-product of node embeddings to measure the similarity between nodes leading to the fact that they lack the capacity to capture complex relationships among nodes. Moreover, they take only structural information but not semantical information when deciding paths in the embedding. In this paper, We propose a novel method called Network Embedding on the Metric of Relation, abbreviated as NEMR, which can learn the embeddings of nodes in a relational metric space efficiently. It first models the relationships among nodes in a metric space with deep learning methods including variational inference that maps the relationship of nodes to a gaussian distribution so as to capture the uncertainties, then infers the embeddings of the nodes by considering not only the equivalence of multiple-paths in order to capture the multiple relationships among nodes that should have the same semantical distance, e.g., age, hobby and profession, but also the natural order of nodes along the path in calculating similarity distance. Experimental results on several public datasets show that the NEMR outperforms the state-of-the-art methods on relevant inference tasks including link prediction and node classification.

HGNN−BRFE: Heterogeneous Graph Neural Network Model Based on Region Feature Extraction

With the strong capability of heterogeneous graphs in accurately modeling various types of nodes and their interactions, they have gradually become a research hotspot, promoting the rapid development of the field of heterogeneous graph neural networks (HGNNs). However, most existing HGNN models rely on meta−paths for feature extraction, which can only utilize part of the data from the graph for training and learning. This not only limits the data generalization ability of deep learning models but also affects the application effect of data−driven adaptive technologies. In response to this challenge, this study proposes a new model—heterogeneous graph neural network based on regional feature extraction (HGNN−BRFE). This model enhances performance through an “extraction−fusion” strategy in three key aspects: first, it efficiently extracts features of neighboring nodes of the same type according to specific regions; second, it effectively fuses information from different regions and hierarchical neighbors using attention mechanisms; third, it specially designs a process for feature extraction and fusion targeting heterogeneous type nodes, ensuring that the rich semantic and heterogeneity information within the heterogeneous graph is retained while maintaining the node’s own characteristics during the node embedding process to prevent the loss of its own features and potential over−smoothing issues. Experimental results show that HGNN−BRFE achieves a performance improvement of 1–3% over existing methods on classification tasks across multiple real−world datasets.

Heterogeneous graph neural network with hierarchical attention for group-aware paper recommendation in scientific social networks

In recent years, the academic groups established in Scientific Social Networks (SSNs) have not only facilitated collaboration among researchers but also enriched the relations in SSNs, providing valuable information for paper recommendation tasks. However, existing paper recommendation methods rarely consider group information and they fail to fully leverage the group information due to the heterogeneous and complex relations between researchers, papers, and groups. In this paper, a heterogeneous graph neural network with hierarchical attention, named HHA-GPR, is proposed for group-aware paper recommendation. Firstly, a heterogeneous graph is constructed based on the interactions of researchers, papers, and groups in SSNs. Secondly, a random walk-based sampling strategy is utilized to sample highly correlated heterogeneous neighbors for researchers and papers. Thirdly, a hierarchical attention network with intra-type and inter-type attention mechanisms is designed to aggregate the sampled neighbors and comprehensively model the complex relations among the heterogeneous neighbors. More specifically, an intra-type attention mechanism is introduced to aggregate the neighbors of the same type, and an inter-type attention mechanism is employed to combine the embeddings of different types to form the ultimate node embedding. Extensive experiments are conducted on the real-world CiteULike and AMiner datasets, and the experimental results demonstrate that our proposed method outperforms other benchmark methods with an average improvement of 5.3 % in Precision, 5.6 % in Recall, and 5.1 % in Normalized Discounted Cumulative Gain (NDCG) across both datasets.

Multi-view graph contrastive representation learning for bundle recommendation

Bundle recommendation can recommend a collection of associated items that can be consumed together to a user rather than recommending these items separately, making it extremely suitable for some scenarios such as product bundle recommendation and game bundle recommendation. Recent bundle recommendation approaches consider auxiliary data to mitigate sparse user-bundle interactions. However, these approaches obtain the node embeddings directly from the established user-bundle graph and do not explicitly exploit the relationships between users (bundles) when constructing recommendation models. Moreover, bundle recommendation approaches based on graph contrastive learning usually construct contrastive views by randomly discarding nodes (edges) in the graph, while discarding some essential nodes or edges will destroy the structure of the original graph, thereby deteriorating the quality of the learned node embeddings. Aiming at these limitations, we propose a bundle recommendation approach based on multi-view graph contrastive representation learning. First, we present a multi-view modeling method to model the relations between entities as several views from different perspectives. These views serve as inputs of graph neural networks for graph representation learning and provide contrastive views for the contrastive learning tasks. Second, we propose a novel framework for bundle recommendation. This framework obtains the user (bundle) embeddings from different views by performing multi-view graph representation learning and enhances the learned user and bundle embeddings through a two-level contrastive learning strategy. On this basis, the enhanced user (bundle) embeddings are fused for prediction. Finally, we design a joint optimization objective to optimize the model parameters, combining the prediction loss that supports multiple negative samples and the contrastive losses. Experiments on the Netease and Youshu datasets reveal that our approach outperforms the state-of-the-art (SOTA) baselines. Furthermore, the average improvements of Recall@K and NDCG@K of our approach over the SOTA baselines are approximately 3.38% and 2.80% on Netease and 3.94% and 4.84% on Youshu.

Heterogeneous graph representation learning via mutual information estimation for fraud detection

In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labelled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and semantic information and embed it into the low-dimensional node representation. Recently, maximizing the mutual information between the local node embedding and the summary representation has achieved the promising results on node classification tasks. However, existing deep graph infomax methods still have the following limitations. Firstly, attribute information of nodes in the graph is not fully utilized for capturing the semantic relationships between nodes. Secondly, the local and global supervision signal are not simultaneously exploited for the node embedding learning. Thirdly, the multiplex heterogeneous relations among nodes are ignored. To address these issues, a heterogeneous graph representation learning model by mutual information estimation (MIE-HetGRL) is proposed in this paper to identify the fraudsters in the fraud review graph. Concretely, a high-order mutual information estimation is proposed to integrate the local and global mutual information as the supervision signal. Then we devise a semantic attention fusion module to aggregate the relation-aware node embeddings into a compact node representation. Finally, a joint contrastive learning is designed for facilitating the training and optimization of model. The experimental results show that our proposed model significantly outperforms state-of-the-art baselines for fraud detection.

Anomaly Detection System Based on Graph Neural Network and Node Embedding for Cybersecurity

Anomaly Detection System Based on Graph Neural Network and Node Embedding for Cybersecurity

Adaptive multi-graph contrastive learning for bundle recommendation

Recently, recommending bundles - sets of items that complement each other - instead of individual items to users has drawn much attention in both academia and industry. Models based on Graph Neural Networks (GNNs) for bundle recommendation have achieved great success in capturing users’ preferences by modeling pairwise correlations among users, bundles, and items via information propagation on graphs. However, a notable limitation lies in their insufficient focus on explicitly modeling intricate ternary relationships. Additionally, the loose combination of node embeddings from different graphs tends to introduce noise, as it fails to consider disparities among the graphs. To this end, we propose a novel approach called Adaptive Multi-Graph Contrastive Learning for Bundle Recommendation (AMCBR). Specifically, AMCBR models ternary interactions by constructing multiple graphs, including a bundle preference graph based on direct user-bundle interactions, a collaborative neighborhoods graph featuring user-level and bundle-level subgraphs, and an item-level preference hypergraph capturing indirect user-bundle relationships through items. Then, (hyper)graph convolution is applied to each (hyper)graph to encode diverse potential preferences into node embeddings. To enhance the model’s robustness, an adaptive aggregation module is employed to assign varying weights to node embeddings from different graphs during the fusion process, which enriches the semantic and comprehensive information in the embeddings while mitigating potential noise. Finally, a contrastive learning strategy is proposed to jointly optimize the model, strengthening collaborative links between individual graphs. Extensive experiments on three real datasets demonstrate that AMCBR can outperform the state-of-the-art baselines on the Top-K recommendations.

Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called Meta-GPS \(++\) . Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-training to extract valuable information from unlabeled nodes. Additionally, we adopt S \({}^{2}\) (scaling and shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS \(++\) . Our code is available here .

EEG-based patient-specific seizure prediction based on Spatial–Temporal Hypergraph Attention Transformer

Background and Objectives:Patient-specific seizure prediction based on Electroencephalogram (EEG) is a challenging and significant neuromedicine study, which could potentially safeguard drug-resistant patients from unforeseen risks in their daily lives. However, prevalent seizure prediction frameworks based on convolutional neural networks (CNNs) prioritize local information while overlooking long-range global dependencies and high-order spatial correlations prevalent among brain leads. Methods:To address the limitations above, we propose a novel Spatial–Temporal Hypergraph Attention Transformer (STHAT) framework for extracting the key features in EEG signals. Aimed at the characteristics of epilepsy EEG data, our framework comprises two primary branches: (1) Long-range Temporal Feature Dependencies: This branch aims to capture temporal dependencies within EEG signals by leveraging Swin Transformer and 2D-CNN. Utilizing shifting windows and convolution operations, we encode local and global content dependencies within segmented EEG samples. (2) High-Order Spatial Information Correlations: We construct a raw graph structure using the Pearson correlation coefficient, enabling the extraction of initial node embeddings through graph convolution operations (GCO). Subsequently, a dynamic hypergraph is formed using K-Nearest Neighbors (KNN) and K-means clustering, allowing us to employ a Hypergraph Attention Network (HAN) to capture structural context relationships among brain regions. By integrating information from both branches, we utilize a linear layer to derive the final seizure prediction outcome. Experiment and Results:Extensive evaluations are conducted on the widely used CHB-MIT dataset. Our comprehensive experimental results demonstrate the effectiveness of the proposed framework in achieving superior performance in automatic seizure data prediction, outperforming state-of-the-art algorithms. The STHAT framework effectively exploits both temporal and spatial information within EEG signals, enhancing seizure prediction accuracy.