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

“…Experiments on the CIC-IDS-2017 dataset demonstrate that TriageHD outperforms state-of-the-art graph neural networks, including graph convolutional networks, graph attention networks, and graph transformer models, in threat prioritization accuracy. …”
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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

“…Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. …”
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CSpredR: A Multi-Site mRNA Subcellular Localization Prediction Method Based on Fusion Encoding and Hybrid Neural Networks by Xiao Wang, Wenshuai Suo, Rong Wang

“…Subsequently, we utilize multi-scale convolutional neural networks and bidirectional long short-term memory networks to capture sequence features, respectively, and fuse the results as input for a multi-head attention mechanism model. …”
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Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents by Yipeng Zhou, Jiaxin Fan, Qingchuan Zhang, Lin Zhu, Xingchen Sun

“…By constructing a document–sentence–entity heterogeneous graph and employing graph convolutional networks (GCNs), the model effectively captures global semantic associations and interactions between cross-sentence triggers and arguments. …”
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Anomaly traffic detection method based on data augmentation and feature mining by AN Yishuai, FU Yu, YU Yihan, LIU Taotao

“…Finally, a multi-layer graph convolutional network with a hierarchical attention mechanism was designed, in which local and global features were hierarchically extracted and fused through a multi-level neighborhood aggregation strategy, significantly enhancing the model’s capability to identify key features. …”
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Feature Coding and Graph via Transformer: Different Granularities Classification for Aircraft by Jianghao Rao, Senlin Qin, Zongyan An, Jianlin Zhang, Qiliang Bao, Zhenming Peng

“…Thanks to the ever-evolving nature of the convolutional neural network (CNN), it has become easier to distinguish and recognize different types of aircraft. …”
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A Unified Graph Theory Approach: Clustering and Learning in Criminal Data by Haifa Al-Ibrahim, Heba Kurdi

“…This study introduces a unified approach integrating spectral graph-based clustering with Graph Convolutional Networks (GCN) to address these challenges. …”
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MB-AGCL: multi-behavior adaptive graph contrast learning for recommendation by Xiaowen lv, Yiwei Zhao, Zhihu Zhou, Yifeng Zhang, Yourong Chen

“…Abstract Graph Convolutional Networks (GCNs) have achieved remarkable success in recommendation systems by leveraging higher-order neighborhoods. …”
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Reliable Event Detection via Multiple Edge Computing on Streaming Traffic Social Data by Yipeng Ji, Jingyi Wang, Yan Niu, Hongyuan Ma

“…We also develop Binary Sample Graph Convolutional Neural Network (BS-GCN) and Binary Sample Graph Attention Network (BS-GAT) to improve the reliability of graph neural network models based on the characteristics of traffic event detection and design an incremental clustering algorithm based on event similarity to implement streaming social traffic event detection. …”
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Linear attention based spatiotemporal multi graph GCN for traffic flow prediction by Yanping Zhang, Wenjin Xu, Benjiang Ma, Dan Zhang, Fanli Zeng, Jiayu Yao, Hongning Yang, Zhenzhen Du

“…This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. …”
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Testing CP properties of the Higgs boson coupling to τ leptons with heterogeneous graphs by W. Esmail, A. Hammad, M. Nojiri, Christiane Scherb

“…We employ three Deep Learning (DL) networks, Multi-Layer Perceptron (MLP), Graph Convolution Network (GCN), and Graph Transformer Network (GTN) to enhance signal-to-background separation. …”
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Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities by Luis Rabelo, Cristian Rincón-Guio, Valeria Laynes, Edgar Gutierrez-Franco, Vasanth Bhat, Juan Zamora-Aguas, Marwen Elkamel

“…Abstract This paper presents a framework for effective last-mile delivery in underdeveloped megacities by combining social media, machine learning, and reinforcement learning. Leveraging a Graph Convolutional Networks and a Long Short-Term Memory model for traffic prediction, the framework incorporates multimodal data sources, such as social media sentiment analysis, to provide real-time insights into traffic dynamics. …”
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Deep learning-based object detection for environmental monitoring using big data by Wenbo Lin, Tingting Li, Xiao Li

“…EGAN constructs a spatiotemporal graph representation that integrates physical proximity, ecological similarity, and temporal dynamics, and applies graph convolutional encoders to learn expressive spatial features. …”
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Optimizing Group Activity Recognition With Actor Relation Graphs and GCN-LSTM Architectures by M. R. Tejonidhi, C. V. Aravinda, S. V. Aruna Kumar, C. K. Madhu, A. M. Vinod

“…By integrating the ARG with a hybrid model that combines Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM), and Attention mechanisms, our approach significantly enhances the extraction of spatial and relational features compared to conventional techniques. …”
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Graph-Based Radiomics Feature Extraction From 2D Retina Images by Ofelio Jorreia, Nuno Goncalves, Rui Cortesao

“…This matrix serves as mathematical representation of the retinal vascular network, constituting a novel form of graph-based radiomic features.…”
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An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects by Shreya Mishra, Neetesh Pandey, Atul Rawat, Divyanshu Srivastava, Arjun Ray, Vibhor Kumar

“…Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. …”
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Leveraging deep learning and graph analysis for enhanced course recommendations in online education by Xinxin Chen, Xiaojing Wang, Ying Wang, Dan Liu, Wei Zhang

“…This research, suggests new hybrid model based on Convolutional Neural Networks (CNNs) with graph analysis to improve online course recommendations by delivering more tailored suggestions to students. …”
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Adapter With Textual Knowledge Graph for Zero-Shot Sketch-Based Image Retrieval by Jie Zhang, Jiali Tang

“…Subsequently, a graph convolutional network (GCN) is used to mine the structural knowledge between nodes, further effectively learning relationships among different categories. …”
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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…The local features extracted by convolutional neural networks are mapped to graph-structured data, and the nodal attention mechanism of GAT is used to capture the global topological association of space objects, which makes up for the deficiency of the convolutional operation in weight allocation and realizes GAT integration. …”
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