FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning by Wei Huang, Junling Chen, Dexian Wang, Pengfei Zhang, Jia Liu, Tianrui Li

“…Also, in the case of multiple knowledge graphs distributed across different clients, it is of interest to ensure that the knowledge graph embedding representations are learned without exposing data and collaboratively. …”
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Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning by Suneeta Satpathy, Pratik Kumar Swain

“…To overcome the limitations of conventional detection strategies, this study proposes the Graph-Contrast Ransomware Detection (GCRD) model comprising Graph-Based Feature Selection (GFS), Contrastive Learning (CLR), and Transformer-Based Classification (FT-Transformer + MLP). …”
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Explainable Recommender Systems Through Reinforcement Learning and Knowledge Distillation on Knowledge Graphs by Alexandra Vultureanu-Albişi, Ionuţ Murareţu, Costin Bădică

“…This study presents a novel framework, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><msup><mi>R</mi><mn>2</mn></msup><msup><mi>K</mi><mn>2</mn></msup><mi>G</mi></mrow></semantics></math></inline-formula> (X for explainability, first R for recommender systems, the second R for reinforcement learning, first K for knowledge graph, the second K stands for knowledge distillation, and G for graph-based techniques), with the goal of developing a next-generation recommender system with a focus on careers empowerment. …”
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Systematic Review of Graph Neural Network for Malicious Attack Detection by Sarah Mohammed Alshehri, Sanaa Abdullah Sharaf, Rania Abdullrahman Molla

“…While several machine learning approaches have been proposed, the use of graph neural networks (GNNs) for cyberattack detection has not yet been systematically explored in depth. …”
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Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning by Yusuf Alaca

“…This study presents a novel approach combining graph-based data representation with DARTS-optimised MobileViT models, with the objective of enhancing diagnostic accuracy and reliability. …”
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Dual Branch Graph Representation Learning-Based Approach for Next Point-of-Interest Recommendation by Guoning Lv, Min Gao

“…Subsequently, it constructs a semantic association graph which preserves the semantic relations between POIs and are further fed into a graph neural network-based backbone to learn the representations of POIs in the semantic feature space. …”
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Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks by Isaac Ampratwum, Amiya Nayak

“…Ensuring robust and efficient service restoration in Wavelength Division Multiplexing (WDM) networks is crucial for maintaining network reliability amidst failures caused by disasters, equipment malfunctions, or power outages. This article presents a hybrid framework that integrates Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to optimize WDM network restoration. …”
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Autonomous air combat decision making via graph neural networks and reinforcement learning by Lin Huo, Chudi Wang, Yue Han

“…To address these challenges, we propose a novel multi-aircraft autonomous decision-making approach based on graphs and multi-agent reinforcement learning (MADRL) under zero-order optimization, implemented through the GraphZero-PPO algorithm. …”
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Construction of Knowledge Graph for Marine Diesel Engine Faults Based on Deep Learning Methods by Xiaohe Tian, Huibing Gan, Yanlin Liu

“…In this paper, we propose a BiLSTM-CRF-based knowledge graph construction method for ship diesel engine faults, aiming at integrating multi-source heterogeneous data through deep learning and knowledge graph technology, and mining the deep semantic associations among fault phenomena, causes, and solutions. …”
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Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs by Zeinab Shahbazi, Rezvan Jalali, Zahra Shahbazi

“…These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. …”
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Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning by Umesh Bhati, Akanksha Sharma, Sagar Gupta, Anchit Kumar, Upendra Kumar Pradhan, Ravi Shankar

“…These networks, validated against extensive experimental data, became input to a Graph Transformer deep learning system. Models were developed for 110 abiotic stress-related TFs, enabling accurate condition-specific detection of TF binding directly from RNA-seq data, bypassing the need for separate ChIP-seq experiments. …”
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A graph neural network simulation of dispersed systems by Aref Hashemi, Aliakbar Izadkhah

“…We present a graph neural network (GNN) that accurately simulates a multidisperse suspension of interacting spherical particles. …”
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Design of an Improved Model for Personalized Adaptive E-Learning Using Context-Aware Federated Learning and Hierarchical Semantic Graph Analysis by Sirisha Udandarao, Krishna Bhukya, Ramesh Chappa

“…Dynamic Reinforcement Meta-Learning Optimization (DRMLO) dynamically structures rewards based on evolved skill graphs to strengthen long-term learning. …”
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Multimodal and Temporal Graph Fusion Framework for Advanced Phishing Website Detection by S. Kavya, D. Sumathi

“…Traditional detection approaches are usually based on single-modal features or static analysis, failing to capture the complex, multi-faceted nature of phishing websites and their dynamic behaviors. Thus, we present a robust Multi-Modal and Temporal Graph Fusion Framework integrating advanced learning paradigms that enhance accuracy and adaptability in phishing detection. …”
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Graph Contrastive Pre-training for Anti-money Laundering by Hanbin Lu, Haosen Wang

“…Abstract Anti-money laundering (AML) is vital to maintaining financial markets, social stability, and political authority. At present, many studies model the AML task as the graph and leverage graph neural network (GNN) for node/edge classification. …”
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CODE CLONE DETECTION WITH SELF-SUPERVISION ON DUAL GRAPHS by Chunguang Li, Adisorn Sirikham, Jessada Konpang, Yan Wang

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Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning by Umar Subhan Malhi, Junfeng Zhou, Abdur Rasool, Shahbaz Siddeeq

“…Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. …”
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Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning by Vijay Govindarajan, Junaid Hussain Muzamal

“…Abstract This paper presents a modular and scalable intrusion detection framework that combines graph-based feature extraction, Transformer-based autoencoding, and contrastive learning to improve detection accuracy in cloud environments. …”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…Additionally, a novel graph learning module is designed to adaptively capture potential correlations between nodes during training. …”
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Discovering Breach Patterns on the Internet of Health Things: A Graph and Machine Learning Anomaly Analysis by Prabin B Lamichhane, Hannah Mannering, William Eberle

“…In this work, we analyze the performance of traditional statistical, machine learning, and graph-based anomaly detection approaches in response to this problem. …”
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