Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation by Chenglin Yu, Lihong Shi, Yangyang Zhao

“…Our approach begins with the construction of trajectory flow graphs using graph convolutional networks (GCNs) to globally capture POI correlations across both spatial and temporal dimensions. …”
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Contaminant Transport Modeling and Source Attribution With Attention‐Based Graph Neural Network by Min Pang, Erhu Du, Chunmiao Zheng

“…In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. …”
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Roof Geometrical Component Extraction Using Bimodal Data and Graph Neural Network by F. Soleimani Vostikolaei, S. Jabari

“…The proposed approach uses convolutional neural networks (CNNs) to extract roof features from both RGB and DSM data. …”
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Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review by S. M. Atoar Rahman, Md Ibrahim Khalil, Hui Zhou, Yu Guo, Ziyun Ding, Xin Gao, Dingguo Zhang

“…In this overview, we review the very new and fundamental models of GNNs and their modifications, such as graph regularized neural networks, graph convolutional neural networks, spatial-temporal graph neural networks, graph attention networks, and their variants in EEG signal analysis fields. …”
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Multiscale Graph Transformer Network With Dynamic Superpixel Pyramid for Hyperspectral Image Classification by Tingting Wang, Yao Sun, Yunfeng Hu

“…To address these limitations, we propose a multi-scale graph transformer network (MSGTN), which captures spatial features at different scales through multiscale graph convolutional networks (GCNs) with adaptive graph structures. …”
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Urban street network morphology classification through street-block based graph neural networks and multi-model fusion by Yang Liu, Qingsheng Guo, Chuanbang Zheng

“…To address this, we propose a novel fusion model that integrates three submodels: our proposed street-block graph neural network (SBGNet), a convolutional neural network (CNN) using ResNet-34, and a multi-layer perceptron (MLP). …”
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User preference modeling for movie recommendations based on deep learning by Yang Gao, Hong Zheng, Haonan Cui

“…While PageRank ranks the films based on their importance in the individual’s history of surfing, Convolutional Neural Network (CNN) predicts the possibility that the movie would be accepted. …”
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Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction by Zhifei Yang, Zeyang Li, Jia Zhang

“…The proposed architecture incorporates two innovative components: 1) a Spatial Similarity Dynamic Graph Convolution (SDGCN) module that adaptively aggregates spatial features through node similarity analysis and time-varying graph structures, and 2) a Bidirectional Double-Cell Recurrent Neural Network (Bi-DouCRNN) that combines LSTM and GRU mechanisms via dual-gating operations to capture multi-scale temporal dynamics. …”
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Event Type and Relationship Extraction Based on Dependent Syntactic Semantic Augmented Graph Networks by Min Zuo, Zexi Song, Qingchuan Zhang, Yueheng Liu, Di Wu, Yuanyuan Cai

“…A new model, Document Event Relationship Extraction based on Graph Convolutional Network with Enhanced Dependency Semantics (GCNEDS) is proposed. …”
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Quantifying the non-isomorphism of global urban road networks using GNNs and graph kernels by Linfang Tian, Weixiong Rao, Kai Zhao, Huy T. Vo

“…This paper trains Graph Neural Networks (GNNs) and graph kernels to classify urban road networks and proposes using graph classification accuracy as a metric to quantify graph non-isomorphism. …”
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Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions by Shiyang Chen, Yang Liu, Qun Zhang, Zhouhang Shao, Zewei Wang

“…This article presents MDST‐GNN, a multi‐distance spatial‐temporal graph neural network for blockchain anomaly detection. …”
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G-KAN: Graph Kolmogorov-Arnold Network for Node Classification Using Contrastive Learning by Lining Yuan

“…Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…To address these challenges, we propose a novel framework that integrates Graph Neural Networks (GNNs) with temporal dynamics. …”
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Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks by Bin Yang, Dan Song, Yadong Li, Jinglong Wang

“…Experimental results reveal that the proposed DGAT method significantly outperforms currently advanced deep learning techniques, including Graph Convolutional Networks, Weave, and Message Passing Neural Networks. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…The FFCGAN model obtained very high results for accuracy, precision, recall, and F1-score, outperforming the commonly applied networks for action recognition, such as the Spatial-Temporal Graph Convolutional Network or its modifications. …”
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Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification by Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed

“…This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). …”
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MRDDA: a multi-relational graph neural network for drug–disease association prediction by Congzhou Chen, Yaozheng Zhou, Yinghong Li, Jin Xu, Demin Li, Lingfeng Wang

“…First, we design a hybrid graph convolutional framework to capture both local and global representations of drugs and diseases. …”
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Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks by Peter Xiangyuan Ma, Keir K. Rogers, Ting S. Li, Renée Hložek, Jeremy J. Webb, Ruth Huang, Julian Meunier

“…The phase space of stellar streams is proposed to detect dark substructure in the Milky Way through the perturbations created by passing subhalos—and thus is a powerful test of the cold dark matter paradigm and its alternatives. Using graph convolutional neural network (GCNN) data compression and simulation-based inference (SBI) on a simulated GD-1-like stream, we improve the constraint on the mass of a [10 ^8 , 10 ^7 , 10 ^6 ] M _⊙ perturbing subhalo by factors of [11, 7, 3] with respect to the current state-of-the-art density power spectrum analysis. …”
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Semantic Fusion-Oriented Bi-Typed Multi-Relational Heterogeneous Graph Neural Network by Yifan Sun, Jing Yan, Lilei Lu, Hongbo Zhang, Yanhong Shang

“…However, existing Heterogeneous Graph Neural Networks (HGNN) primarily focus on HGs with single relationships and are ineffective for BMHGs. …”
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