MultiGLICE: Combining Graph Neural Networks and Program Slicing for Multiclass Software Vulnerability Detection by Wesley de Kraker, Harald Vranken, Arjen Hommersom

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DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform by Shuli Sun, Jixin Liu, Guojun Li, Bingqiang Liu

“…However, high dropout rates and noise hinder accurate spatial domain identification for understanding tissue architecture. We present DeepGFT, a method that simultaneously models spot-wise and gene-wise relationships by integrating deep learning with graph Fourier transform for spatial domain identification. …”
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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

“…To address these issues, this paper proposes a graph attention-based reinforcement learning algorithm, named GAPO. …”
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A transfer learning-based graph convolutional network for dynamic security assessment considering loss of synchronism of wind turbines and unknown faults by Sasan Azad, Mohammad Taghi Ameli, Amjad Anvari-Moghaddam, Miadreza Shafie-khah

“…Pre-fault DSA methods that utilize deep learning techniques have been successfully implemented and have shown promising results. …”
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DLProv: a suite of provenance services for deep learning workflow analyses by Débora Pina, Liliane Kunstmann, Adriane Chapman, Daniel de Oliveira, Marta Mattoso

“…Deep learning (DL) workflows consist of multiple interdependent and repetitive steps, including data preparation, model training, evaluation, and deployment. …”
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Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML by Subhash Ariyadasa, Shantha Fernando, Subha Fernando

“…Recent studies have shown that machine learning has become prominent in the present anti-phishing context, and the techniques like deep learning have extensively improved anti-phishing tools’ detection ability. …”
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KG4Py: A toolkit for generating Python knowledge graph and code semantic search by Lu Liang, Yong Li, Ming Wen, Ying Liu

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A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network by Rokin Maharjan, Korn Sooksatra, Tomas Cerny, Yudeep Rajbhandari, Sakshi Shrestha

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GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network by Kwangsu Kim, Jihwan Ha

“…These methods have been widely applied in areas such as user-item recommendations, gene-gene interactions, and lncRNA-disease association prediction. In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. …”
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Shape-based disease grading via functional maps and graph convolutional networks with application to Alzheimer’s disease by Julius Mayer, Daniel Baum, Felix Ambellan, Christoph von Tycowicz, for the Alzheimer’s Disease Neuroimaging Initiative

“…This work aims to alleviate these limitations by adapting the concept of functional maps. Further, we present a graph-based learning approach for morphometric classification of disease states that uses novel shape descriptors based on this concept. …”
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A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems by Shuaibo Wang, Xinyuan Xiang, Jie Zhang, Zhuohang Liang, Shufang Li, Peilin Zhong, Jie Zeng, Chenguang Wang

“…To address these challenges, this paper presents a multi-task learning framework based on spatiotemporal graph convolutional networks that efficiently performs both tasks. …”
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LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs by Dimitrios P. Panagoulias, Evangelia-Aikaterini Tsichrintzi, Dionisios N. Sotiropoulos, Konstantina Chrysafiadi, Evangelos Sakkopoulos, George A. Tsihrintzis, Maria Virvou

“…This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and validatable cross-cultural lyric analysis. …”
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Optimizing IoT Intrusion Detection—A Graph Neural Network Approach with Attribute-Based Graph Construction by Tien Ngo, Jiao Yin, Yong-Feng Ge, Hua Wang

“…While graph deep-learning-based methods have shown promise in cybersecurity applications, existing approaches primarily construct graphs based on physical network connections, which may not effectively capture node representations. …”
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Investigating and Optimizing MINDWALC Node Classification to Extract Interpretable Decision Trees from Knowledge Graphs by Maximilian Legnar, Joern-Helge Heinrich Siemoneit, Gilles Vandewiele, Jürgen Hesser, Zoran Popovic, Stefan Porubsky, Cleo-Aron Weis

“…This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. …”
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StyleGraph: A Heterogeneous Graph Neural Framework for Stylistic and Semantic Rumor Detection on Social Media by Haider Jaffar, Ali Mohades, Mohammad Ebrahim Shiri

“…The growing prevalence of deceptive content presents a significant challenge that necessitates advanced detection techniques, beyond classical machine learning. …”
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Advanced QSPR modeling of profens using machine learning and molecular descriptors for NSAID analysis by W. Eltayeb Ahmed, Muhammad Farhan Hanif, Muhammad Kamran Siddiqui, Brima Gegbe

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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. …”
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PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences by Amin Khodaei, Sania Eskandari, Hadi Sharifi, Behzad Mozaffari-Tazehkand

“…On the other hand, it has been demonstrated that the use of graph algorithms and machine learning in the analysis and examination of virus samples and even viral variants can yield beneficial results. …”
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HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning by Jinchen Sun, Haoran Zheng

“…Abstract Background Drug–drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. …”
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A Dual-Perspective Self-Supervised IoT Intrusion Detection Method Based on Topology Reconstruction and Feature Perturbation by Ruisheng Li, Huimin Shen, Qilong Zhang

“…Yet, traditional models struggle to capture the complex topological structures in IoT environments, and their training often relies heavily on large amounts of labeled data, making them unsuitable for IoT settings where massive data is continually generated. This paper presents a graph-based framework leveraging self-supervised learning to address these challenges. …”
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