Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. …”
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SMS spam detection using BERT and multi-graph convolutional networks by Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma

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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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MulGCN: MultiGraph Convolutional Network for Aspect-Level Sentiment Analysis by Huyen Trang Phan, Van Du Nguyen, Ngoc Thanh Nguyen

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Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances by Xiaoyin Xu, Zhumei Luo, Menglong Feng

“…Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose and model intertwined spatio-temporal dependencies. Current methods typically treat wind power as a unified signal without explicitly separating inherent patterns from external influences, so they have limited prediction accuracy. …”
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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

“…The ASTAM employs multi-temporal gated convolution with multi-scale temporal input segments to model complex non-linear temporal correlations. It utilizes static and dynamic parallel multi-graphs to facilitate the modeling of complex spatial dependencies. …”
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A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction by Yunyang Huang, Hongyu Yang, Zhen Yan

“…Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. …”
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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. …”
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PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding by Zhihua Wen, Yunchun Dong, Lihong Peng, Longxin Zhang, Junfeng Yan

“…Furthermore, most datasets in the field of TCM suffer from limited data volumes, which can adversely impact model training. Methods To tackle these challenges, we present a prescription recommendation framework called PRDAGE, which is based on data augmentation and multi-graph embedding. …”
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Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity by Yue Chen, Jian Lu

“…Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. …”
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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

“…Combining multimodal backgrounds with large language models offers prospects for alleviating pain points. …”
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Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction by Juan Chen, Rui Huang

“…In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. …”
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Research on the Condition Assessment Method of Mechanical Seal Based on Fusing Multi-Graph Neural Networks From Decision Layer by Xiaoran Zhu, Binhui Wang, Junchao Chen, Zipeng Li

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Vessel Type Recognition Using a Multi-Graph Fusion Method Integrating Vessel Trajectory Sequence and Dependency Relations by Lin Ye, Xiaohui Chen, Haiyan Liu, Ran Zhang, Bing Zhang, Yunpeng Zhao, Dewei Zhou

“…In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. …”
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A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity by Qingjie Xu, Qingjie Xu, Libing An, Haiqiang Yang, Haiqiang Yang, Keum-Shik Hong, Keum-Shik Hong

“…This study aims to address these limitations by developing a novel graph-based deep learning model that fully utilizes both functional and structural information from multi-frequency EEG data.MethodsThis paper introduces a Multi-Frequency EEG data-based Multi-Graph Convolutional Network (MF-MGCN) model for AD diagnosis. …”
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MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction by Yong Zhang, Yong Yin, Ning Xu, Bowen Jia

“…Experimental results demonstrate that the MCE-HGCN model converges effectively with small datasets. …”
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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…In the spatial dimension, the model incorporates a spatial embedding module and a multi-graph convolutional module. …”
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A fuzzy soft planar graph with application in image segmentation by Waheed Ahmad Khan, Arsh E. Mah Niaz, Trung Tuan Nguyen, Minh Hoan Pham, Thi Minh Ngoc Tong, Hai Van Pham

“…Abstract Fuzzy sets and soft sets are two distinct mathematical tools used for modeling real-world problems involving uncertainty. …”
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Feature Enhanced Spatial–Temporal Trajectory Similarity Computation by Silin Zhou, Chengrui Huang, Yuntao Wen, Lisi Chen

“…To solve this problem, we propose a Feature Enhanced Spatial–Temporal trajectory similarity computation framework FEST, which is a graph neural network (GNN) and sequence model pipeline. We first use the GNN model to capture global information on the road network. …”
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Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion by Chenxi LIU, Dong WANG, Huiling CHEN, Renfa LI

“…By effectively capturing the spatio-temporal characteristics of urban private car travel, a multi-source heterogeneous data fusion model for private car volume prediction was proposed.Firstly, private car trajectory and area-of-interest data were integrated.Secondly, the spatio-temporal correlations between private car travel and urban areas were modeled through multi-view spatio-temporal graphs, the multi-graph convolution-attention network (MGC-AN) was proposed to extract the spatio-temporal characteristics of private car travel.Finally, the spatio-temporal characteristics and external characteristics such as weather were integrated for joint prediction.Experiments were conducted on real datasets, which were collected in Changsha and Shenzhen.The experimental results show that, compared with the existing prediction model, the root mean square error of the MGC-AN is reduced 11.3%～20.3%, and the average absolute percentage error is reduced 10.8%～36.1%.…”
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