Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation by WEI Chuyuan, YUAN Baojie, WANG Changdong

“…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. Secondly, a    local-level basket-item graph is constructed to learn the disentangled multi-intents within baskets via a graph disentangled network, and subsequently the multi-intents are encoded via a multi-head self-attention layer to obtain the final intent representations. …”
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Infant cry classification using an efficient graph structure and attention-based model by Qiao X., Jiao S., Li H.

“…In this paper, an efficient graph structure based on multi-dimensional hybrid features is proposed. …”
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Integrating deformable CNN and attention mechanism into multi-scale graph neural network for few-shot image classification by Yongmin Liu, Fengjiao Xiao, Xinying Zheng, Weihao Deng, Haizhi Ma, Xinyao Su, Lei Wu

“…The feature extraction module of graph neural networks has always been designed as a fixed convolutional neural network (CNN), but due to the intrinsic properties of convolution operations, its receiving domain is limited. …”
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Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction by Mengting Niu, Chunyu Wang, Yaojia Chen, Quan Zou, Ximei Luo

“…Therefore, we constructed a heterogeneous graph network MiGNN2CDS on the basis of multi-instance learning (MIL). …”
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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

“…Additionally, we developed a multi-layer embedding method for symptoms and herbs, using Sentence Bert (SBert) and graph convolutional networks. …”
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Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients by Yinan Wang, Yinan Wang, Lizhou Gong, Yang Zhao, Yewei Yu, Hanxu Liu, Xiao Yang

“…This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients. …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. …”
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ACGRHA-Net: Accelerated multi-contrast MR imaging with adjacency complementary graph assisted residual hybrid attention network by Haotian Zhang, Qiaoyu Ma, Yiran Qiu, Zongying Lai

“…This graph is then combined with a residual hybrid attention network, forming the adjacency complementary graph assisted residual hybrid attention network (ACGRHA-Net) for multi-contrast MR image reconstruction. …”
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Urban street network morphology classification through street-block based graph neural networks and multi-model fusion by Yang Liu, Qingsheng Guo, Chuanbang Zheng

“…To address this, we propose a novel fusion model that integrates three submodels: our proposed street-block graph neural network (SBGNet), a convolutional neural network (CNN) using ResNet-34, and a multi-layer perceptron (MLP). …”
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MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction by Zuhai Hu, Jinxiang Yang, Linghao Ni, Liyuan Zhang, Bin Peng

“…Abstract Accurate prediction of drug side effect frequencies is critical for drug safety assessment but remains challenging due to the high cost of clinical trials and the limited generalizability of existing models. We propose Multi Fingerprint and Graph Embedding model (MultiFG), a novel deep learning framework that integrates diverse molecular fingerprint types, graph-based embeddings, and similarity features of drug–side effect pairs. …”
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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

“…These graph structures are then processed through graph convolutional networks (GCNs), which integrate various sources of information within the graphs to obtain behavioral representations of vessel trajectories. …”
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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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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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Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding by Yelan Wu, Pugang Cao, Meng Xu, Yue Zhang, Xiaoqin Lian, Chongchong Yu

“…To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. …”
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Harnessing Syntax GCN and Multi-View Interaction for Conversational Aspect-Based Quadruple Sentiment Analysis by Chunling Wu, Houwei Kang

“…To address this, this paper introduces the Syntax and Consecutive Multi-View Network. This model captures the syntactic dependencies among utterances using Graph Convolutional Networks (GCN) and employs multi-view interactions along with three consecutive multi-head attention modules to construct contextual connections within the dialogue. …”
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ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks by Lin Zhu, Yi Fang, Shuting Liu, Hong-Bin Shen, Wesley De Neve, Xiaoyong Pan

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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling by Chaoyi Li, Hongxin Xiang, Wenjie Du, Tengfei Ma, Haowen Chen, Xiangxiang Zeng, Lei Xu

“…GraphGIM is pre-trained on 2 million 2D graphs and multi-view 3D geometry images through contrastive learning. …”
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Smart City Traffic Flow and Signal Optimization Using STGCN-LSTM and PPO Algorithms by Tuxiang Lin, Rongliang Lin

“…This study presents a novel framework that integrates the Spatiotemporal Graph Convolutional Network-Long Short-Term Memory (STGCN-LSTM) model for traffic flow prediction with the Proximal Policy Optimization (PPO) algorithm for dynamic traffic signal control. …”
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Predicting noncoding RNA and disease associations using multigraph contrastive learning by Si-Lin Sun, Yue-Yi Jiang, Jun-Ping Yang, Yu-Han Xiu, Anas Bilal, Hai-Xia Long

“…The third step is to use an encoder with a Graph Convolutional Network (GCN) architecture to extract embedding vectors. …”
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Detection and recognition of unsafe behaviors of underground coal miners based on deep learning by GUO Xiaoyuan, ZHU Meiqiang, TIAN Jun, ZHU Beibei

“…Subsequently, a spatiotemporal graph convolutional network (ST-GCN) was utilized for behaviour recognition. …”
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