Rumor Detection Based on Knowledge Enhancement and Graph Attention Network by Wanru Wang, Yuwei Lv, Yonggang Wen, Xuemei Sun

“…This paper used knowledge graphs to enhance topics and learn the text features by using self-attention. …”
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Weak Preprocessing Iris Feature Matching Based on Bipartite Graph by Jin Zhang, Kangwei Wang, Rongrong Shi, Feng Xie, Qinghe Zheng, Ruizhe Zhang, Cheng Wu, Yiming Wang

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Malicious Traffic Detection on Tofino Using Graph Attention Model by Xichang Gao, Lizhuang Tan, Shengpeng Chen, Peiying Zhang, Jian Wang

“…Subsequently, Maltof runs a lightweight Edge-based Graph Attention Network model on the CPU data plane of the switch, performing in-depth analysis on suspicious packets identified in the initial screening, learning and capturing complex relational features in network traffic to further determine whether malicious behavior is present. …”
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Maritime Traffic Knowledge Discovery via Knowledge Graph Theory by Shibo Li, Jiajun Xu, Xinqiang Chen, Yajie Zhang, Yiwen Zheng, Octavian Postolache

“…By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. …”
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Explainable Graph Neural Networks for Power Grid Fault Detection by Richard Bosso, Corey Chang, Mahdi Zarif, Yufei Tang

“…Lack of transparency significantly hinders power utility operations, as interpretability is crucial to building trust, accountability, and actionable insights. This research presents a comprehensive framework that systematically evaluates state-of-the-art explanation strategies, representing the first use of such a framework for Graph Neural Network models for defect location detection. …”
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Spatial Proximity Relations-Driven Semantic Representation for Geospatial Entity Categories by Yongbin Tan, Hong Wang, Rongfeng Cai, Lingling Gao, Zhonghai Yu, Xin Li

“…Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge and help achieve the deep fusion of geospatial data. …”
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The songbird connectome (OSCINE-NET.ORG): structure–function organization beyond the canonical vocal control network by Andrew Savoy, Katherine L. Anderson, Joseph V. Gogola

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Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis by Feifeng Jiang, Jun Ma

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MambaCAttnGCN+: a comprehensive framework integrating MambaTextCNN, cross-attention and graph convolution network for piRNA-disease association prediction by Dengju Yao, Xiangkui Li, Xiaojuan Zhan, Bo Zhang, Jian Zhang

“…We integrated piRNA sequence information, disease-related semantic terms, and existing piRNA-disease association networks to construct a heterogeneous graph. Utilizing the Mamba module, we developed an innovative sequence embedding model, MambaTextCNN, to extract features from piRNA sequences, which we used as node attributes within the heterogeneous graph. …”
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Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network by Wu Feng, Xiulin Geng, Xiaoyu He, Miao Hu, Jie Luo, Meihua Bi

“…A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. …”
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Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning by Liangcai Zhou, Long Huo, Linlin Liu, Hao Xu, Rui Chen, Xin Chen

“…Each agent is responsible for regulating the output of generation units in a specific area, and together, the agents work to achieve the global OPF objective, which reduces the complexity of the DRL model’s training process. Additionally, a graph neural network (GNN) is integrated into the DRL framework to capture spatiotemporal features such as RES fluctuations and power grid topological structures, enhancing input representation and improving the learning efficiency of the DRL model. …”
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TriageHD: A Hyper-Dimensional Learning-to-Rank Framework for Dynamic Micro-Segmentation in Zero-Trust Network Security by Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong, Nathaniel D. Bastian, Mohsen Imani

“…To address these challenges, micro-segmentation has proven to be an effective defense strategy for isolating network components and limiting breach propagation. This paper presents TriageHD, a novel framework that integrates graph-based Hyper-Dimensional Computing (HDC) with a learning-to-rank algorithm to strengthen zero-trust network security. …”
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Limits of Depth: Over-Smoothing and Over-Squashing in GNNs by Aafaq Mohi ud din, Shaima Qureshi

“…Graph Neural Networks (GNNs) have become a widely used tool for learning and analyzing data on graph structures, largely due to their ability to preserve graph structure and properties via graph representation learning. …”
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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System by Safdar Hussain Javed, Maaz Bin Ahmad, Muhammad Asif, Waseem Akram, Khalid Mahmood, Ashok Kumar Das, Sachin Shetty

“…However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. …”
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Social Network Analysis: A Novel Paradigm for Improving Community Detection by Rodrigo Hernández, Inmaculada Gutiérrez, Javier Castro

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Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation by Maher Marwani, Georges Kaddoum

“…As a result, the performance of DL models may be suboptimal when applied to wireless environments due to the failure to capture the network’s non-Euclidean geometry. This study presents a novel approach to address the challenge of power control and spectrum allocation in an N-link interference environment with shared channels, utilizing a graph neural network (GNN) based framework. …”
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A spatiotemporal graph wavelet neural network for traffic flow prediction by Linjie Zhang, Jianfeng Ma

“…Moreover, our work has better learning performance by employing the connection and interaction of graphs.…”
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Graph-informed convolutional autoencoder to classify brain responses during sleep by Sahar Zakeri, Somayeh Makouei, Sebelan Danishvar

“…Eighteen types of different stimuli including instrumental and natural sounds were presented to participants during REM. The selected significant features were used to train a novel deep-learning classifiers. …”
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Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology by Olga Fourkioti, Matt De Vries, Reed Naidoo, Chris Bakal

“…To address this, many graph-based methods have been proposed, where each WSI is represented as a graph with tiles as nodes and edges defined by specific spatial relationships. …”
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Intelligent analysis of the quality of educational content on statistics of student performance in the learning management system "Scorina" by A. G. Savenko

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