LMGDoc: Light Multigranular GNN for Efficient Document Understanding by Abdellatif Sassioui, Yasser Elouargui, Mohamed El Kamili, Rachid Benouini, El Mehdi Benyoussef, Meriyem Chergui, Mohammed Ouzzif

“…In this paper, we propose LMGDoc, a lightweight model capable of understanding and processing VRDs composed of three main components: a Feature Extractor using GloVe for text embedding and a novel layout encoding method, a Graph Constructor for representing document structures at multiple granular levels (word, region, and page), and a Graph Convolution Network (GNN). …”
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D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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Physics-informed deep learning with Kalman filter mixture for traffic state prediction by Niharika Deshpande, Hyoshin (John) Park

“…Central to our approach is the development of a novel deep learning (DL) model called the physics informed-graph convolutional gated recurrent neural network (PI-GRNN). …”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…Further augmenting its capability, TGAMTSA utilizes a Hybrid Architecture that synergistically combines 1D Convolutional Neural Networks (CNNs) and a dual arrangement of Quad Long Short-Term Memory (LSTM) and Quad Gated Recurrent Units (GRU) networks. …”
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Study on Quality Assessment Methods for Enhanced Resolution Graph-Based Reconstructed Images in 3D Capacitance Tomography by Robert Banasiak, Mateusz Bujnowicz, Anna Fabijańska

“…However, given the recent advancements in Graph Convolutional Neural Networks (GCNs) for improving ECT image reconstruction, reliable Quality Assessment methods are essential for comparing the performance of different GCN models. …”
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Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems by Bing Dong, Haoran Yao, Chuang Luo, Ruichao Yang, Ziyue Wang

“…To address this challenge, we used raw NOTAM data from the Civil Aviation Information Center, from September 2021 to September 2023. A trained relational graph convolutional neural network (CEV-RGCN) model, along with a tianzege-convolutional neural network (CNN), were employed to calculate the feature similarity of Chinese characters based on phonetics and glyphs. …”
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Faster Dynamic Graph CNN: Faster Deep Learning on 3D Point Cloud Data by Jinseok Hong, Keeyoung Kim, Hongchul Lee

“…However, it has been difficult to apply such data as input to a convolutional neural network (CNN) or recurrent neural network (RNN) because of their unstructured and unordered features. …”
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Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation by WEI Chuyuan, YUAN Baojie, WANG Changdong

“…  Firstly, a global-level user-item interaction graph is constructed. Considering that user behavior changes over time, a long and short-term time decay weight is designed to balance the importance of the interaction items, and then the user’s dynamic interests are learnt through graph convolution networks. …”
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Complex Network Analytics for Structural–Functional Decoding of Neural Networks by Jiarui Zhang, Dongxiao Zhang, Hu Lou, Yueer Li, Taijiao Du, Yinjun Gao

“…This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. …”
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Intelligent accounting question-answering robot based on a large language model and knowledge graph by Shi Shengyun, Li Guoxi, Wang Yong

“…This study aims to design an intelligent accounting question-answering robot based on a large language model and knowledge graph. To build a complete knowledge graph, this study uses the attention mechanism and convolutional neural network to build a connection prediction model and completes the accounting question-answering knowledge graph. …”
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The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning by Shen Jiang, Ningning Shi, Chang Liu

“…Abstract This work proposes a personalized music learning platform model based on deep learning, aiming to provide efficient and customized learning recommendations by integrating audio, video, and user behavior data. This work uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to extract audio and video features, while using multi-layer perceptrons to encode user behavior data. …”
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Redundant Path Optimization in Smart Ship Software-Defined Networking and Time-Sensitive Networking Networks: An Improved Double-Dueling-Deep-Q-Networks-Based Approach by Yanli Xu, Songtao He, Zirui Zhou, Jingxin Xu

“…Central to this framework is the introduction of a redundant multipath selection algorithm, which leverages Double Dueling Deep Q-Networks (D3QNs) in conjunction with Graph Convolutional Networks (GCNs). …”
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Optimizing Personalized Recommender Systems for Teachers’ Digital Learning Models Using Deep Learning Algorithms by Jun Zhong, Wenjuan Zhang

“…Specifically, the approach integrates DINA cognitive diagnosis and gray partial correlation evaluation to construct a student model that captures students’ mastery of knowledge points and cognitive ability levels. Additionally, a graph convolutional neural network (GCN) is employed, leveraging the sequential relationships between subject knowledge points to automatically capture semantic information from the higher-order structure of knowledge points, thereby enabling personalized recommendations. …”
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Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing by Yaowen Lu, Xiankun Zhang, Huitao Zhang

“…We then utilize the structural information from the question–concept graph, applying graph convolutional networks to derive question embeddings that integrate both textual semantics and structural information. …”
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A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations by Beste Ustubioglu, Gul Tahaoglu, Arda Ustubioglu, Guzin Ulutas, Muhammed Kilic

“…Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. …”
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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

“…Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal scales, which restricts effective future flow prediction; (2) reliance on predefined graph structures in graph neural networks, making it challenging to accurately model the spatial relationships in complex road networks; and (3) end-to-end training, which often results in unclear optimization directions for model parameters, thereby limiting improvements in predictive performance. …”
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Application of BERT-GCN Model Based on Strong Link Relation Graph in Water Use Enterprise Classification by Junhong Xiang, Baoxian Zheng, Chenkai Cai, Shuiping Yao, Shang Gao

“…First, we constructed a co-word relation graph based on the typical industry characteristics keywords extracted by the <i>TF-IDF</i> and extracted co-word relation features using a graph convolutional network (GCN). …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions by Asmaa A. Abdelwahab, Mustafa A. Elattar, Sahar Ali Fawzi

“…This review provides a comprehensive exploration of how graph-based computational techniques, including Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have emerged as powerful tools for modeling complex CYP enzyme interactions and predicting ADMET properties with improved precision. …”
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Robust Anomaly Detection of Multivariate Time Series Data via Adversarial Graph Attention BiGRU by Yajing Xing, Jinbiao Tan, Rui Zhang, Jiafu Wan

“…Hence, this paper proposes a robust multivariate temporal data anomaly detection method based on graph attention for training convolutional neural networks (PGAT-BiGRU-NRA). …”
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