GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…The local features extracted by convolutional neural networks are mapped to graph-structured data, and the nodal attention mechanism of GAT is used to capture the global topological association of space objects, which makes up for the deficiency of the convolutional operation in weight allocation and realizes GAT integration. …”
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SDDGRNets: Level–Level Semantically Decomposed Dynamic Graph Reasoning Network for Remote Sensing Semantic Change Detection by Zhuli Xie, Gang Wan, Yunxia Yin, Guangde Sun, Dongdong Bu

“…Semantic change detection technology based on remote sensing data holds significant importance for urban and rural planning decisions and the monitoring of ground objects. However, simple convolutional networks are limited by the receptive field, cannot fully capture detailed semantic information, and cannot effectively perceive subtle changes and constrain edge information. …”
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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

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Cross Attentive Multi-Cue Fusion for Skeleton-Based Sign Language Recognition by Ogulcan Ozdemir, Inci M. Baytas, Lale Akarun

“…We demonstrate how the proposed attention-based framework exposes distinct temporal patterns of visual cue representations extracted via Spatio-Temporal Graph Convolutional Network (ST-GCN) and exploits them for learning SL representations more effectively. …”
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I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…A novel fusion-aware encoder assimilates both explicit and implicit user feedback to uncover latent preferences driving POI choices. The model combines a multi-head self-attention transformer to capture the sequential and temporal dynamics of user behavior, with a graph convolutional network (GCN) that models complex co-visitation patterns among POIs. …”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…For human body keypoints, we introduce the Multi-scale Features and Frame-Attention Adaptive Graph Convolutional Network (MSF-AGCN) to extract irregular and impulsive motion features. …”
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Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction by Hongkai Zhang, Hongxuan Yuan, Minghao Shao, Junxin Wang, Suhong Liu

“…Our network employs a parallel “local–global” encoding scheme: the local stream uses depth-wise separable convolutions to capture fine-grained details, while the global stream integrates a Swin-Transformer with a graph-attention module (Swin-GAT) to model long-range contextual and topological relationships. …”
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DSGRec: dual-path selection graph for multimodal recommendation by Zihao Liu, Wen Qu

“…Although methods based on graph convolutional networks (GCNs) have achieved notable success, they still face two key limitations: (1) the narrow interpretation of interaction information, leading to incomplete modeling of user behavior, and (2) a lack of fine-grained collaboration between user behavior and multi-modal information. …”
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CSpredR: A Multi-Site mRNA Subcellular Localization Prediction Method Based on Fusion Encoding and Hybrid Neural Networks by Xiao Wang, Wenshuai Suo, Rong Wang

“…Subsequently, we utilize multi-scale convolutional neural networks and bidirectional long short-term memory networks to capture sequence features, respectively, and fuse the results as input for a multi-head attention mechanism model. …”
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Graph Neural Network Learning on the Pediatric Structural Connectome by Anand Srinivasan, Rajikha Raja, John O. Glass, Melissa M. Hudson, Noah D. Sabin, Kevin R. Krull, Wilburn E. Reddick

“…While graph neural networks (GNNs), specifically graph convolutional networks (GCNs), have gained popularity lately for their effectiveness in learning on graph data, achieving strong performance in adult sex classification tasks, their application to pediatric populations remains unexplored. …”
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Deep learning model for patient emotion recognition using EEG-tNIRS data by Mohan Raparthi, Nischay Reddy Mitta, Vinay Kumar Dunka, Sowmya Gudekota, Sandeep Pushyamitra Pattyam, Venkata Siva Prakash Nimmagadda

“…This study presents a novel approach that integrates electroencephalogram (EEG) and functional near-infrared spectroscopy (tNIRS) data to enhance emotion classification accuracy. A Modality-Attentive Multi-Channel Graph Convolution Model (MAMP-GF) is introduced, leveraging GraphSAGE-based representation learning to capture inter-channel relationships. …”
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Histopathological Image Analysis Using Deep Learning Framework by Sudha Rani Vupulluri, Jogendra Kumar Munagala

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Robust graph fusion and recognition framework for fingerprint and finger‐vein by Zhitao Wu, Hongxu Qu, Haigang Zhang, Jinfeng Yang

“…The feature extraction method based on graph structure can well solve the problem of feature space mismatch for the finger bi‐modalities, and the end‐to‐end fusion recognition can be realised based on graph convolutional neural networks (GCNs). …”
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Graph Learning-Based Power System Health Assessment Model by Koji Yamashita, Nanpeng Yu, Evangelos Farantatos, Lin Zhu

“…The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. …”
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A dual path graph neural network framework for dementia diagnosis by Denghui Zhang, Chenxuan Zhu

“…In order to more effectively represent brain networks, we designed specialized correlation matrixs to reinforce the constructed graph. We then performed multi-scale graph convolution to analyze brain connectivity at varying resolutions-from fine-grained to more extensive patterns, and ultimately employed an attention mechanism to enhance features across different domains. …”
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Water Quality Prediction Method Based on Reinforcement Learning Graph Neural Network by Mingming Yan, Zhe Wang

“…To address these issues, we propose a reinforcement learning graph neural network-based approach. Our method, an adjacency reinforcement learning, and multi-channel graph convolution autoencoder, predicts water quality by performing reinforcement learning on the adjacency of water quality indicator images. …”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

“…Thus, a pedestrian trajectory prediction model based on a self - supervised spatiotemporal graph network is proposed. Firstly, in the process of spatiotemporal graph modeling, this model introduces hop interaction instead of node interaction to update node features, which greatly reduces the times of graph convolution operations, alleviates the problem of feature smoothing, and greatly improves the accuracy of prediction. …”
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Testing CP properties of the Higgs boson coupling to τ leptons with heterogeneous graphs by W. Esmail, A. Hammad, M. Nojiri, Christiane Scherb

“…We employ three Deep Learning (DL) networks, Multi-Layer Perceptron (MLP), Graph Convolution Network (GCN), and Graph Transformer Network (GTN) to enhance signal-to-background separation. …”
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Decoding Gestures in Electromyography: Spatiotemporal Graph Neural Networks for Generalizable and Interpretable Classification by Hunmin Lee, Ming Jiang, Jinhui Yang, Zhi Yang, Qi Zhao

“…Harnessing these tailored graph structures, we present Graph Convolution Network (GCN)-based classification models adept at effectively extracting and aggregating key features associated with various gestures. …”
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Multi-criteria path rationalization in the conditions of multi-type passenger transport systems by V. V. Egorov

“…As a result, the study obtained algorithms for solving single-criteria and multi-criteria problems on graphs. For multicriterial problems, the author used the convolution method and the method of ordering criteria by the degree of decreasing their significance. …”
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