Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

“…This paper presents an advanced graph convolutional network model, enhanced with Wasserstein distance-based adversarial learning (WD-ACGN), addressing the limitations of existing single-station and less explored multi-station water level forecasting approaches. …”
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Reinforcement Learning-Based Formulations With Hamiltonian-Inspired Loss Functions for Combinatorial Optimization Over Graphs by Redwan Ahmed Rizvee, Raheeb Hassan, Md. Mosaddek Khan

“…Though PI-GNN is highly scalable, it exhibits a noticeable decrease in terms of the number of satisfied constraints with higher graph densities. In this paper, firstly, we identify the limitations and empirically present our strategy to improve PI-GNN’s performance. …”
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Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection by Nuwan Madusanka, Hadi Sedigh Malekroodi, H. M. K. K. M. B. Herath, Chaminda Hewage, Myunggi Yi, Byeong-Il Lee

“…This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. …”
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A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery by Qing Ye, Yundian Zeng, Linlong Jiang, Yu Kang, Peichen Pan, Jiming Chen, Yafeng Deng, Haitao Zhao, Shibo He, Tingjun Hou, Chang‐Yu Hsieh

“…However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in biomedical data. In this study, Knowledge‐Guided Drug Relational Predictor (KGDRP), a graph representation learning approach is developed that effectively integrates multimodal biomedical data, including network data containing biological system information, gene expression data, and sequence data that incorporates chemical molecular structures, all within a heterogeneous graph (HG) structure. …”
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Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction by Yu Liang, Dalei Wu

“…This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. …”
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Advanced intrusion detection in internet of things using graph attention networks by Aamir S. Ahanger, Sajad M. Khan, Faheem Masoodi, Ayodeji Olalekan Salau

“…Common Internet of Things safety features like encryption, authentication, and access control frequently fall short of meeting their desired functions. In this paper, we present a novel perspective to IoT security by using a Graph-based (GB) algorithm to construct a graph that is evaluated with a graph-based learning Intrusion Detection System (IDS) incorporating a Graph Attention Network (GAT). …”
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Graph-text contrastive learning of inorganic crystal structure toward a foundation model of inorganic materials by Keisuke Ozawa, Teppei Suzuki, Shunsuke Tonogai, Tomoya Itakura

“…However, there is a lack of studies on inorganic materials due to the difficulty in the comprehensive representation of geometric concepts composing crystals: local atomic environments, their connections, and the global symmetries. We present a contrastive learning of inorganic crystal structure (CLICS) for embedding the geometric concepts, which contrasts texts representing the contextual patterns of geometries with the crystal graphs. …”
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Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning by Evgeny Y. Pavlenko

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Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks by Amarudin Daulay, Kalamullah Ramli, Ruki Harwahyu, Taufik Hidayat, Bernardi Pranggono

“…In this paper, we propose FedGCL, a secure and efficient FL framework integrating contrastive graph representation learning for enhanced feature discrimination, a Jain-index-based fairness-aware aggregation mechanism, an adaptive synchronization scheduler to optimize communication rounds, and secure aggregation via homomorphic encryption within a Trusted Execution Environment. …”
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A Novel Assembly Process Knowledge Graph Inference Method Integrating Logical Rules and Embedded Learning by Peilin Shao, Zhicheng Huang, Yongqiang Wan, Lihong Qiao, Xinzheng Xu, Chao Chen, Zhujia Li, Nabil Anwer, Yifan Qie

“…The high complexity, repeatability, standardization, and quality requirements of the assembly process presently put forward raised requirements for the unified expression and organization of assembly process knowledge. …”
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DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions by Chao Cao, Mengli Li, Chunyu Wang, Lei Xu, Quan Zou, Yansu Wang, Wu Han

“…Results To address these issues, we innovatively propose a novel deep graph collaboration learning method for circRNA-miRNA interaction, called DGCLCMI. …”
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PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph by Naeima Hamed, Omer Rana, Pablo Orozco-terWengel, Benoît Goossens, Charith Perera

“…Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. …”
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Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks by Ze Chang, Yunfei Cai, Xiao Fan Liu, Zhenping Xie, Yuan Liu, Qianyi Zhan

“…In this paper, we propose a novel graph neural network method to overcome class imbalance issues by improving the Graph Attention Network (GAT) and incorporating ensemble learning concepts. …”
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GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning by Zhouhang Shao, Xuran Wang, Enkai Ji, Shiyang Chen, Jin Wang

“…E-commerce platforms face significant challenges in detecting anomalous products, including counterfeit goods and fraudulent listings, which can undermine user trust and platform integrity. This paper presents Graph Neural Network-based E-commerce Anomaly Detection via Dual-stage Learning (GNN-EADD), a novel approach leveraging graph neural networks for anomaly detection in large-scale e-commerce ecosystems. …”
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ReAHGN: Adaptive Heterogeneous Graph Neural Network With Relation-Aware Embedding by Xiaoyu Zhu, Xinzhe Yu, Enze Zha, Shiyang Lin

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Interpretation of chemical reaction yields with graph neural additive network by Youngchun Kwon, Yongsik Jung, Youn-Suk Choi, Seokho Kang

“…Prediction of chemical yields is crucial for exploring untapped chemical reactions and optimizing synthetic pathways for targeted compounds. Recently, graph neural networks have proven successful in achieving high predictive accuracy. …”
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HRRPGraphNet++: Dynamic Graph Neural Network with Meta-Learning for Few-Shot HRRP Radar Target Recognition by Lingfeng Chen, Zhiliang Pan, Qi Liu, Panhe Hu

“…High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition. …”
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Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning by Johannes Pauli, Maximilian Hoffmann, Ralph Bergmann

“…In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. …”
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GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning by Tianhao Peng, Qiang Yue, Yu Liang, Jian Ren, Jie Luo, Haitao Yuan, Wenjun Wu

“…In this paper, we propose a novel Global-local Optimized grAph Transformer framework for collaborative learning, termed GOAT. …”
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stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning by Yupeng Xu, Hao Dai, Jinwang Feng, Keren Xu, Qiu Wang, Pingting Gao, Chunman Zuo, Chunman Zuo

“…However, batch effects, unbalanced reference annotations, and tissue heterogeneity pose significant challenges to alignment analysis. Here, we present stGuide, an attention-based supervised graph learning model designed for cross-slice alignment and efficient label transfer from reference to query datasets. stGuide leverages supervised representations guided by reference annotations to map query slices into a shared embedding space using an attention-based mechanism. …”
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