Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</sc>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. …”
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Graph Neural Network With Hessian-Based Locally Linear Embedding for Cancer Metastasis Analysis in Lymph Nodes Using DeepLab Segmentation by Senthil Jayapal, R. Annamalai

“…The proposed framework integrates DeepLab, a highly advanced convolutional neural network for image segmentation, to segment the lymph node regions from high-resolution MRI and PET scans. …”
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SGRD: A Ship Group Relationship Description Method Based on Scene Graph Generation With a Global-Local Context Fusion Network by Qianwen Rui, Yanan You, Jingyi Cao, Kaiwen Zhu, Yuanyuan Qiao

“…To address this, we propose a ship group relationship description (SGRD) method based on remote sensing SGG with a global and local context fusion network, called GLFN. The proposed network integrates global feature fusion through a transformer-based self-attention mechanism and enhances local feature fusion using a graph convolutional network focused on object-specific graph structures. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
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Using Hybrid Neural Networks to Improve Traffic Prediction and Congestion Management by Ali Abd Samir

“…The proposed Materials and methods integrates Diffusion Convolutional Recurrent Neural Network (DCRNN) with graph-based models, allowing information to be shared among related sensors over large distances. …”
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A novel approach for detecting malicious hosts based on RE-GCN in intranet by Haochen Xu, Xiaoyu Geng, Junrong Liu, Zhigang Lu, Bo Jiang, Yuling Liu

“…For malicious host detection, this paper proposes the Relational-Edge Graph Convolutional Network (RE-GCN) model, which can directly aggregate and learn features on edges and use them to accurately classify nodes, compared to other GNN models. …”
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Attention-Based Hypergraph Neural Network: A Personalized Recommendation by Peihua Xu, Maoyuan Zhang

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Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data by Lian Duan, Mahshad Hashemi, Alioune Ngom, Luis Rueda

“…While standard GNN models like Graph Convolutional Networks (GCN), GraphSAGE, Graph Attention Networks (GAT), and MixHop often assume homophily (similar nodes are more likely to be connected), this assumption does not always hold in biological networks. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Through systematic evaluation across 10 graph convolutional layers, GAT demonstrated optimal performance, achieving average ARI advantages of 0.108 and 0.112 over alternative graph convolutional layers in VGAE and GNODEVAE architectures respectively, along with ASW advantages of 0.047 and 0.098. …”
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Hyperspectral image super-resolution via joint network with spectral-spatial strategy by Yaxin Dong, Bo Yang, Cong Liu, Zemin Geng, Taiping Wang

“…While existing approaches typically stack convolutional layers to increase network depth, they frequently overlook the structured continuity of spectral bands and non-local spatial similarities, resulting in limited performance and overfitting risks. …”
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Intelligent recognition method for urban road grid patterns by fusing mesh and road features by Zhekun Huang, Haizhong Qian, Zhongxiang Cai, Andong Wang, Junwei Wang, Feng Xiong

“…A model is constructed using topology adaptive graph convolutional networks to obtain preliminary RGP recognition results. …”
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Spatio-Temporal Collaborative Perception-Enabled Fault Feature Graph Construction and Topology Mining for Variable Operating Conditions Diagnosis by Jiaxin Zhao, Xing Wu, Chang Liu, Feifei He

“…Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. …”
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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning by Huimin Luo, Huimin Luo, Hui Yang, Hui Yang, Ge Zhang, Ge Zhang, Jianlin Wang, Jianlin Wang, Junwei Luo, Chaokun Yan, Chaokun Yan, Chaokun Yan

“…Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. …”
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A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works.ResultsOur review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. …”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…To validate the efficacy of the generated graph, we utilized a fast Graph Convolutional Network (GCN) for ECG recognition. …”
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Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection by Haowei Lou, Zesheng Ye, Lina Yao, Yu Zhang

“…Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. …”
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A method for classifying objects with a heterogeneous set of information features based on the generation of graph-chromatic maps by A.S. Bozhday, L.N. Gorshenin

Self-Supervised Knowledge-Aware Recommendation Model Integrating Adaptive Hypergraph by ZHOU Jiaxuan, LIU Xianhui, ZHAO Xiaodong, HOU Wenlong, ZHAO Weidong

“…The model first utilizes a hybrid graph convolutional network to jointly learn the low-order interaction embeddings in the interaction graph and the higher-order interaction embeddings in the adaptive hypergraph. …”
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Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning by Wei Shuai, Xue Wu, Chen Chen, Enguang Zuo, Xiaomei Chen, Zhengfang Li, Xiaoyi Lv, Lijun Wu, Cheng Chen

“…Four classification models, namely artificial neural network (ANN), convolutional neural network (CNN), improved AlexNet model, and multi-scale convolutional neural network (MSCNN) were established. …”
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DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection by Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir

“…The architecture comprises a Graph Attention Module (GAM) to capture spatial dependencies using multi-head graph attention and graph convolution layers, and a Channel Attention Module (CAM) to amplify discriminative features and suppress irrelevant information. …”
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