A Max-Flow Approach to Random Tensor Networks by Khurshed Fitter, Faedi Loulidi, Ion Nechita

“…In the case of series-parallel graphs, an explicit formula for the limiting eigenvalue distribution is provided using classical and free multiplicative convolutions. …”
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A Mechanical Fault Identification Method for On-Load Tap Changers Based on Hybrid Time—Frequency Graphs of Vibration Signals and DSCNN-SVM with Small Sample Sizes by Yanhui Shi, Yanjun Ruan, Liangchuang Li, Bo Zhang, Yichao Huang, Mao Xia, Kaiwen Yuan, Zhao Luo, Sizhao Lu

“…Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. …”
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BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data by Zhang Wen, Junjie Zhao, An Zhang, Wenhao Bi, Boyu Kuang, Yu Su, Ruixin Wang

“…Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. …”
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Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach by Marc Semper, Manuel Curado, Jose Luis Oliver, Jose F. Vicent

“…Several complementary approaches are compared: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs). …”
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Scheduling framework based on reinforcement learning in online-offline colocated cloud environment by Ling MA, Qiliang FAN, Ting XU, Guanchen GUO, Shenglin ZHANG, Yongqian SUN, Yuzhi ZHANG

“…Some reinforcement learning-based scheduling algorithms for cloud computing platforms barely considered one scenario or ignored the resource constraints of jobs and treated all machines as the same type, which caused low resource utilization or insufficient scheduling efficiency.To address the scheduling problems in online-offline colocated cloud environment, a framework named JobFusion was proposed.Firstly, an efficient resource partitioning scheme was built in the cloud computing platform supporting virtualization technology by integrating the hierarchical clustering method with connectivity constraints.Secondly, a graph convolutional neural network was utilized to embed the attributes of elastic dimension with various constraints and the jobs with various numbers, to capture the critical path information of workflow.Finally, existing high-performance reinforcement learning methods were integrated for scheduling jobs.According to the results of evaluation experiments, JobFusion improves the resource utilization by 39.86% and reduces the average job completion time by up to 64.36% compared with baselines.…”
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Identification of Alzheimer’s disease brain networks based on EEG phase synchronization by Jiayi Cao, Bin Li, Xiaoou Li

“…Abstract Objective Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer’s disease (AD) and to assist in the early diagnosis of Alzheimer’s disease. …”
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A Deep Neural Network-Based Approach to Media Hotspot Discovery by Pan Luo

“…Finally, the text feature representation method based on graph convolutional neural network is combined with the clustering algorithm based on the moving range density maximum selection method to build a deep learning-based media hotspot discovery framework. …”
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Time–frequency ensemble network for wind turbine mechanical fault diagnosis by Haiyu Guo, Xingzheng Guo, Xiaoguang Zhang, Fanfan Lu, Chuang Liang

“…Second, the Transformer and Graph Convolutional Network (GCN) are combined to extract the time–frequency discriminative features of defects. …”
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Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks by Zhongbo Bai, Xiaomei Bai

“…Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif-based graph networks, finding the best collaborative partners in a sports team with attention-aware graph networks, and finding the rising star for a sports team with attribute-based convolutional neural networks. …”
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EMFF-Net: Edge-Enhancement Multi-Scale Feature Fusion Network by Xuhui Guan, Jiwang Zhou, Jian Chen, Xiaodan Xu, Yizhang Jiang, Kaijian Xia

“…From these extracted features, we generate a global mapping graph as a bootstrap region. Additionally, we introduce Spatial Channel Convolution (SCEConv) and Reverse Gated Channel Transformer (RGCT) to incorporate boundary information into the segmentation network. …”
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Unmanned aerial vehicle–assisted node localization for wireless sensor networks by Xu Yang, Zhenguo Gao, Qiang Niu

“…Then, in the non-occluded node localization phase, the convolutional neural network technique is employed to identify the nodes. …”
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Plasticity in inhibitory networks improves pattern separation in early olfactory processing by Shruti Joshi, Seth Haney, Zhenyu Wang, Fernando Locatelli, Hong Lei, Yu Cao, Brian Smith, Maxim Bazhenov

“…Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. …”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…The latter based on GCRL is developed by inserting Long Short-Term Memory network (LSTM) into Graph Convolutional Network (GCN), which can effectively extract the spatial and temporal features of the sensor data for anomaly detection. …”
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An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks by Zhifeng Ma, Zhanjun Hao, Zhenya Zhao

“…According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. …”
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Multimodal fusion based few-shot network intrusion detection system by Congyuan Xu, Yong Zhan, Zhiqiang Wang, Jun Yang

“…The G-Model employs convolutional neural networks to capture spatial connections in traffic feature graphs, while the S-Model uses the Transformer architecture to process and fuse network feature sets with long-range dependencies. …”
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PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks by Wei-Cheng Gu, Bin-Guang Ma

“…PGBTR consists of two main components: the input generation step PDGD (Probability Distribution and Graph Distance) and the deep learning model CNNBTR (Convolutional Neural Networks for Bacterial Transcriptional Regulation inference). …”
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AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics by Jian Feng, Lang Yu, Rui Ma

“…Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. …”
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MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting by Sunlei Qian, Xiaorong Zhu

“…To address this, we propose MeshHSTGT, a novel hierarchical spatio-temporal framework that synergizes TimesNet for multi-periodic temporal-frequency modeling and a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware spatial-temporal dependency learning. …”
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Multi-feature stock price prediction by LSTM networks based on VMD and TMFG by Zhixin Zhang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…Finally, the filtered features are modeled and predicted using a Long Short-Term Memory (LSTM) network. Experimental results demonstrate that the VMD–TMFG–LSTM model significantly outperforms AutoRegressive Integrated Moving Average (ARIMA), Neural Network (NN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), as well as single LSTM, TMFG–LSTM, and VMD–LSTM models in forecasting the closing prices of multiple stocks. …”
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Visualization Methods for DNA Sequences: A Review and Prospects by Tan Li, Mengshan Li, Yan Wu, Yelin Li

“…Additionally, we summarize machine learning techniques applicable to sequence visualization, such as graph embedding methods and the use of convolutional neural networks (CNNs) for processing graphical representations. …”
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