Using Graph Neural Networks in Reinforcement Learning With Application to Monte Carlo Simulations in Power System Reliability Analysis by Oystein Rognes Solheim, Boye Annfelt Hoverstad, Magnus Korpas

“…This paper presents a novel method for power system reliability studies that combines graph neural networks with reinforcement learning. …”
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Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms by Huong To Duong, Van Hai Ho, Phuc do

“…ViKGFC integrates a Knowledge Graph (KG), inference rules, and the Knowledge graph - Bidirectional Encoder Representations from Transformers (KG-BERT) deep learning model. …”
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Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images by Mengmeng Li, Xinyi Gai, Kangkai Lou, Alfred Stein

“…A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. …”
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Stable Variable Fixation for Accelerated Unit Commitment via Graph Neural Network and Linear Programming Hybrid Learning by Linfeng Yang, Peilun Li, Shifei Chen, Haiyan Zheng

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Intelligent vulnerability detection system based on graph structured source code slice by Deqing ZOU, Xiang LI, Minhuan HUANG, Xiang SONG, Hao LI, Weiming LI

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations by Hang Wei, Jialu Hou, Yumeng Liu, Alexey K. Shaytan, Bin Liu, Hao Wu

“…Conclusions The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance.…”
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Ganet: graph attention based Terracotta Warriors point cloud completion network by Jian Gao, Yuhe Zhang, Gaoxue Shiqin, Pengbo Zhou, Yue Wen, Guohua Geng

“…The Terracotta Warriors, as important cultural heritage, present a challenging test case due to damage and missing parts caused by prolonged burial and environmental factors. …”
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Domain‐adaptation‐based named entity recognition with information enrichment for equipment fault knowledge graph by Dengrui Xiong, Xinyu Li, Liang Gao, Yiping Gao

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Graph neural networks for mechanical property prediction of 2D fiber composites by Erdem Caliskan, Reza Abedi, Massimiliano Lupo Pasini

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Multi-stream part-fused graph convolutional networks for skeleton-based gait recognition by Likai Wang, Jinyan Chen, Zhenghang Chen, Yuxin Liu, Haolin Yang

“…To be specific, we integrate a channel attention learning mechanism into the graph convolutional networks (GCN) to improve the representational power. …”
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MSTT: A Multi-Spatio-Temporal Graph Attention Model for Pedestrian Trajectory Prediction by Qingrui Zhang, Xuxiu Zhang, Zilang Ye, Jing Mi

“…To overcome this challenge, we present a relative spatio-temporal encoding (RSTE) strategy that proficiently captures and analyzes this essential information. …”
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AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic by Junhao Liu, Guolin Shao, Hong Rao, Xiangjun Li, Xuan Huang

“…To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. …”
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Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. by Quang Dung Dinh, Daniel Kunk, Truong Son Hy, Vamsi Nalam, Phuong D Dao

“…However, the traditional manual analysis of EPG waveform data is time-consuming and labor-intensive, limiting research throughput. This study presents a novel Machine Learning (ML) approach to automate the annotation of EPG signals. …”
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Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning by Wenfeng Dai, Yanhong Wang, Shuai Yan, Qingzhi Yu, Xiang Cheng

“…Abstract Reliable prediction of drug–target interaction (DTI) is essential for accelerating drug discovery, yet remains hindered by data imbalance, limited interpretability, and neglect of protein dynamics. Here, we present GHCDTI, a heterogeneous graph neural framework designed to overcome these challenges through three synergistic innovations. …”
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Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks by Nabeel Al-Qirim, Munir Majdalawieh, Anoud Bani-hani, Hussam Al Hamadi

“…This study presents a novel “Digital-twin and Machine Learning-based SCADA Cyber Threat Intelligence (DT-ML-SCADA-CTI)” approach, which utilizes an innovative algorithm to visualize and predict the effects of cyber-attacks, including FDIA, RTCI, and SRA, on SCADA systems. …”
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A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities by Philip Kwaku Adjei, Qin Zhiguang, Isaac Amankona Obiri, Ansu Badjie, Christian Nii Aflah Cobblah, Ali Alqahtani, Yeong Hyeon Gu, Mugahed A. Al-antari

“…However, detecting vulnerabilities in smart contract interactions remains challenging due to complex state interdependencies. This paper presents a novel approach using multi-agent Reinforcement Learning (MARL) to identify smart contract vulnerabilities. …”
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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized optimization for customized production by Chengcai He, Jiaxing Deng, Jingchun Wu, Beicheng Qin, Jinxiang Chen, Yan Li, Qiangsheng Huang

“…Additionally, reinforcement learning and graph neural networks are used to efficiently extract and utilize manufacturing knowledge. …”
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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…To address these challenges, this research presents an innovative framework known as the Continual Learning-based Spatial–Temporal Graph Convolutional Recurrent Neural Network (STGNN-CL) for persistent and accurate long-term traffic flow prediction. …”
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Transforming formal knowledge to language and graphs to promote mathematics learning: A repeated-measures mixed design quasi-experiment by Inka Sara Hähnlein, Clara Luleich, Philipp Reiter, Nils Waterstraat, Pablo Pirnay-Dummer

“…The transition from school to university mathematics presents a significant challenge for students, as both the demands on mathematical reasoning and the level of abstraction increase. …”
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Travel route recommendation with a trajectory learning model by Xiangping Wu, Zheng Zhang, Wangjun Wan

“…Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. …”
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