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Granular concept-enhanced relational graph convolution networks for link prediction in knowledge graph

INFORMATION SCIENCES 2025年 694卷

作者： Dai, Yuhao Yan, Mengyu Li, Jinhai Kunming Univ Sci & Technol Fac Sci Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Data Sci Res Ctr Kunming 650500 Yunnan Peoples R China

Link prediction is a task of completing absent triplets by leveraging existing triplets in KG and the ultimate goal is to mitigate the incompleteness and sparsity of KG in terms of content. As well known, Relational graph convolutional networks (R-GCN) model is a promising method for link prediction due to its capability of the graph structure. However, R-GCN mainly relies on information from adjacent nodes, leading to shortcomings in the model for capturing a wider range of relational information. Meanwhile, Formal Concept Analysis (FCA) is increasingly being applied in various fields as an effective data analysis tool. Inspired by this, we integrate FCA into R-GCN to address the shortcomings of R-GCN mentioned above. Specifically, a formal context is first created according to the entities and relations, and granular concepts can be obtained by the formal context. Then the weights of the relational parameters in R-GCN are redistributed based on similarity of granular concepts. Further we develop granular concept-enhanced relational graph convolution networks (GCR-GCN) model, where granular concept can simulate the process of data conceptualization in human brain very well, so it has stronger interpretability compared to the black-box characteristic of R-GCN. Finally, experimental results demonstrate that the GCRGCN model improves the effectiveness of link prediction by effectively assigning different weights to entities with the same relation. In addition, the granular concept effectively improves the computational efficiency of the model and reduces the storage pressure of the model during computation.

关键词： Knowledge graph Link prediction Formal concept analysis graph convolution networks Granular computing

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Visibility graph and graph convolution networks-based segmentation of carbon emission in China

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ANNALS OF OPERATIONS RESEARCH 2025年第1期348卷 609-630页

作者： Hu, Jun Chu, Chengbin Criado, Regino Chen, Junhua Hao, Shuya Wang, Maoze Fuzhou Univ Sch Econ & Management Fuzhou 350108 Fujian Peoples R China Univ Rey Juan Carlos Dept Matemat Aplicada CC Ingn Mat & Tecnol Elect ESCET Tulipan s-n Madrid 28933 Spain Univ Gustave Eiffel ESIEE Paris COSYS GRETTIA F-77454 Marne la Vallee France Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100081 Peoples R China

Carbon emissions drive climate change. Especially with the rapid development of economy, carbon emissions are increasing in recent years, and the carbon emission data sets are more comprehensive. How to analyze the data is important. Furthermore, to find the main characteristics of carbon emission, we propose a new method of segmentation in the time series that adopts communities finding in complex network, graph convolution networks (GCN) and visibility graph (VG). Experiments on carbon emission datasets show that the detector has better detection performance than existing graph connectivity-based detectors. In addition, we find that combining the results of GCN segmentation can highlight economic geographic attributes such as resource endowment, industrial structure, and market demand in carbon emission regions, thus complementing the existing applications of complex network methods in the energy field and providing insights for decision support of carbon emissions.

关键词： Carbon emission Data analysis graph convolution networks Visibility graph Complex network Community detection

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CrossDomain Recommendation Based on MetaData Using graph convolution networks

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IEEE ACCESS 2023年 11卷 90724-90738页

作者： Khan, Rabia Iltaf, Naima Latif, Rabia Jamail, Nor Shahida Mohd Natl Univ Sci & Technol Dept Comp Software Engn Islamabad 46000 Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia

Recent advancements in the domain of recommender systems have stemmed from the inspiration of representing the user-item interaction into graphs. These heterogeneous graphs comprehensively capture the non-linear relationships between users and items alongwith features and emneddings. graph convolution networks (GCNs) are state-of-the-art graph-based learning models that learn and represent the graph structures by recursively stacking layers of convolution and non-linear activation operations. GCNs are augmented with the strength of deep learning paradigms resulting in achieving better performance as compared to traditional CF (Collaborative Filtering) methods. Despite modern improvements in the domain of recommender systems, cold-start users are a daunting challenge in the design of recommender systems since the conventional recommendation services are based on solely one data source. During the recent years, cross-domain recommendation methods have gained popularity because of the availability of information in multiple domains for cold start users. We supplement this information by utilizing the data contained in the metadata of users alongwith the strength of modelling graphs using GCNs. Our proposed algorithm seams the strength of GCNs with cross-domain paradigm utilizing the richness in metadata in user's feedback to overcome the sparsity in user-item rating matrix. The combined advantages of GCNs and cross-domain approaches alleviated the issues of cold-start users by transferring user preferences from a source domain to a target domain.

关键词： Cross-domain recommendation collaborative filtering graph based representation deep learning coldstart users graph convolution networks

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Learning of 3D graph convolution networks for Point Cloud Analysis

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第8期44卷 4212-4224页

作者： Lin, Zhi-Hao Huang, Sheng Yu Wang, Yu-Chiang Frank Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan

Point clouds are among the popular geometry representations in 3D vision. However, unlike 2D images with pixel-wise layouts, such representations containing unordered data points which make the processing and understanding the associated semantic information quite challenging. Although a number of previous works attempt to analyze point clouds and achieve promising performances, their performances would degrade significantly when data variations like shift and scale changes are presented. In this paper, we propose 3D graph convolution networks (3D-GCN), which uniquely learns 3D kernels with graph max-pooling mechanisms for extracting geometric features from point cloud data across different scales. We show that, with the proposed 3D-GCN, satisfactory shift and scale invariance can be jointly achieved. We show that 3D-GCN can be applied to point cloud classification and segmentation tasks, with ablation studies and visualizations verifying the design of 3D-GCN.

关键词： Three-dimensional displays Feature extraction convolution Kernel Shape Two dimensional displays Task analysis 3D vision point clouds deformable kernels graph convolution networks 3D classification 3D segmentation

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PedAST-GCN: Fast Pedestrian Crossing Intention Prediction Using Spatial-Temporal Attention graph convolution networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13277-13290页

作者： Ling, Yancheng Ma, Zhenliang Zhang, Qi Xie, Bangquan Weng, Xiaoxiong South China Univ Technol Sch Civil Engn Transportat Guangzhou 510000 Peoples R China KTH Royal Inst Technol Dept Civil & Architectural Engn S-11428 Stockholm Sweden

Accurately and timely predicting pedestrian crossing intentions in real-time is critical for operating intelligent vehicles on roads. Although existing models achieve promising accuracy using complex models and video image data, they are constrained for real-time practical use given the high model complexity, time-consuming data preprocessing, and low-quality image data in the wild. To address these, the paper proposes a Spatial-Temporal Attention graph convolution Network model for fast pedestrian crossing intention prediction (PedAST-GCN). It uses a lightweight GCN model as the backbone network with simple but robust graph representations of pedestrian crossing intention modality features, including pedestrian pose, bounding box, and vehicle speeds. The model is validated by comparing it with state-of-the-art models on two large-scale public datasets (JAAD and PIE). The results highlight the better performance of the PedAST-GCN model for pedestrian crossing intention prediction in terms of accuracy and computation times. The ablation analysis confirms the value of the backbone layer and graph design, the designed modality features, the effectiveness of attention mechanisms in capturing long-term dependencies (spatial-temporal attention) and fusing heterogeneous features (modality attention), and the robust performance across various observation lengths and in the presence of noisy data.

关键词： Pedestrians Predictive models Data models Feature extraction Computational modeling Skeleton Data mining Pedestrian crossing intention prediction graph convolution networks modality features video image data

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Incorporating multi-interest into recommendation with graph convolution networks

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第11期37卷 9192-9212页

作者： Jiang, Nan Zeng, Zilin Wen, Jie Zhou, Jie Liu, Ziyu Wan, Tao Liu, Ximeng Chen, Honglong East China Jiaotong Univ Coll Informat Engn Nanchang 330013 Jiangxi Peoples R China East China Jiaotong Univ Coll Elect & Automat Engn Nanchang Jiangxi Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China China Univ Petr Coll Control Sci & Engn Qingdao Peoples R China

In recent years, the appearance of graph convolutional networks (GCNs) provides a new idea for graph structure data processing. Because of that, they can learn excellent user and item embedding by using cooperative signals of high-order neighbors, and the GCNs technique shows great potential in the recommendation. The common problem with the bulk of GCN-based models is that it appears the situation of performance degradation during the stacking of network layers. The recently proposed IMP-GCN alleviates this problem to some extent. It aims to avoid the influence of downside information from high-order propagation on embedding learning. However, we consider that it ignores the multi-interest factor, in which users may have different interests. In this paper, we present a multi-interest GCN(MI-GCN) model for a recommendation, and it conducts high-order graph convolution operations in three sets of subgraphs. Users with similar interests and the corresponding interaction items belong to the identical subgraph. As for the formation of the subgraph, we adopt two varied clustering methods and the user feature to form a subgraph generation mechanism. This mechanism can generate three groups of differential subgraphs to divide users into multi-interest groups and make subgraph division more reasonable. We carry out massive experiments on three real-world datasets, demonstrating the effectiveness of our model. Experimental results confirm that our presented MI-GCN outperforms the state-of-the-art GCN-based recommendation models.

关键词： cluster graph convolution networks multi-interest recommendation subgraph

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Long-Term Demand Prediction for Public Bicycle Sharing System: A Spatio-Temporal Attentional graph convolution networks Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 21515-21527页

作者： Liu, Ziheng Gokon, Hideomi Sekimoto, Yoshihide Univ Tokyo Grad Sch Engn Tokyo 1538505 Japan Japan Adv Inst Sci & Technol Grad Sch Adv Sci & Technol Nomi Ishikawa 9231211 Japan Univ Tokyo Ctr Spatial Informat Sci Tokyo 1538505 Japan

Accurately predicting the long-term demand for public bicycle systems (PBS) is crucial for policy implementations such as operator rebalancing. With the continuous advancement of deep learning techniques, including self-attention mechanisms and graph convolution networks (GCNs), we can better understand the nonlinear relationship of spatio-temporal data. Despite increasing interest in long-series transportation forecasting, there are very few long-term spatio-temporal predictions focused on PBS data. In this paper, we introduce a novel spatio-temporal attentional graph convolutional network (ST-AGCN) designed to predict demand accurately over a 24-hour horizon, addressing the limitations of existing models and the characteristics of PBS data. The core innovation of our model is the integration of a probabilistic self-attention mechanism (Probsparse self-attention) for predicting long-term temporal dependencies and Chebyshev GCN for extracting complex spatial dependencies within the encoder-decoder framework. This integration significantly reduces computational time. Additionally, we devise a PBS data-based spatial weighting calculation method to generate the spatio-temporal graph, which facilitates capturing more inter-station relationships to improve computational efficiency and accuracy. Experimental results compared with seven baseline models on real-world datasets demonstrate that ST-AGCN outperforms state-of-the-art baselines in both accuracy and computational efficiency.

关键词： Deep learning self-attention graph convolution networks bike-sharing systems demand prediction

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Spatial-Temporal Position-Aware graph convolution networks for Traffic Flow Forecasting

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8650-8666页

作者： Zhao, Yiji Lin, Youfang Wen, Haomin Wei, Tonglong Jin, Xiyuan Wan, Huaiyu Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

Recent works demonstrate that capturing correlations between road network nodes is crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal, and joint spatial-temporal correlations between two nodes, whose strength is related to spatial and temporal position factors. For example, traffic congestion that occurs at a traffic hub has a wider and stronger impact than that at a branch road. Moreover, the above impacts can vary with temporal position. Although spatial-temporal graph convolution networks have become a popular paradigm for modeling those correlations, there are still three problems with existing models: (i) failing to effectively model joint spatial-temporal correlations;(ii) ignoring spatial and temporal position factors when modeling the aforementioned correlations;and (iii) failing to capture distinct spatial-temporal patterns of each node. To cope with the above issues, this paper proposes a novel Spatial-Temporal Position-aware graph convolution Network (STPGCN) for traffic flow forecasting. Specifically, a trainable embedding module is constructed to represent the spatial and temporal positions of the nodes. Subsequently, a spatial-temporal position-aware relation inference module is proposed to adaptively infer the correlation weights of the three important spatial-temporal relations. Based on this, the generated spatial-temporal relations are integrated into a graph convolution layer for aggregating and updating node features. Finally, we design a spatial-temporal position-aware gated activation unit in the graph convolution, to capture the node-specific pattern features under the guidance of position embedding. Extensive experiments on six real-world datasets demonstrate the superiority of our model in terms of prediction performance and computational efficiency.

关键词： Correlation convolution Forecasting Predictive models Feature extraction Roads Data models Traffic flow forecasting spatial-temporal correlations spatial-temporal position graph convolution networks

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SA-BiGCN: Bi-Stream graph convolution networks With Spatial Attentions for the Eye Contact Detection in the Wild

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第2期25卷 2089-2100页

作者： Ling, Yancheng Ma, Zhenliang Xie, Bangquan Zhang, Qi Weng, Xiaoxiong South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510000 Peoples R China KTH Royal Inst Technol Dept Civil & Architectural Engn S-11428 Stockholm Sweden

Eye contact is essential in transmitting information and intention in the wild environment (e.g., urban streets or parking lots) with mixed vehicles and pedestrians. Compared with the vision image data, the human skeleton data are deemed to be robust to unconstrained surroundings and illumination. However, the skeleton graph-based approaches are mainly used for the action recognition. It is challenging to directly apply them to the eye detection task, which is momentary and dynamic given the complex wild environment. This paper proposes a Bi-stream Spatial Attention graph convolution Network (SA-BiGCN) for eye contact detection in the wild. We design a directed, nose-centric skeleton graph to capture relevant and hierarchical information and their interactions. We also propose a Bi-stream graph convolution network model with spatial attention to dynamically extract and fuse skeleton joints and bones information. The model was validated by comparing with state-of-art models on three large-scale public datasets, including JAAD, PIE, and LOOK. The results highlight the accuracy and generalization performance of the proposed SA-BiGCN model in detecting the eye contact in the wild environment. The ablation analysis validates the importance of the skeleton graph design, the spatial attention mechanism in the feature fusion process, as well as the model robustness against noisy skeleton data in terms of part occlusions, block occlusions, random occlusions, and random deviations.

关键词： graph convolution networks spatial attention graph convolution networks spatial attention

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Predicting miRNA-disease associations based on 1ncRNA-miRNA interactions and graph convolution networks

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BRIEFINGS IN BIOINFORMATICS 2023年第1期24卷 bbac495-bbac495页

作者： Wang, Wengang Chen, Hailin East China Jiaotong Univ Sch Software Nanchang Peoples R China

Increasing studies have proved that microRNAs (miRNAs) are critical biomarkers in the development of human complex diseases. Identifying disease -related miRNAs is beneficial to disease prevention, diagnosis and remedy. Based on the assumption that similar miRNAs tend to associate with similar diseases, various computational methods have been developed to predict novel miRNA-disease associations (MDAs). However, selecting proper features for similarity calculation is a challenging task because of data deficiencies in biomedical science. In this study, we propose a deep learning -based computational method named MAGCN to predict potential MDAs without using any similarity measurements. Our method predicts novel MDAs based on known 1ncRNA-miRNA interactions via graph convolution networks with multichannel attention mechanism and convolutional neural network combiner. Extensive experiments show that the average area under the receiver operating characteristic values obtained by our method under 2 -fold, 5 -fold and 10 fold cross -validations are 0.8994, 0.9032 and 0.9044, respectively. When compared with five state-of-the-art methods, MAGCN shows improvement in terms of prediction accuracy. In addition, we conduct case studies on three diseases to discover their related miRNAs, and find that all the top 50 predictions for all the three diseases have been supported by established databases. The comprehensive results demonstrate that our method is a reliable tool in detecting new disease -related miRNAs.

关键词： miRNA-disease associations lncRNA-miRNA interactions graph convolution networks multichannel attention mechanism CNN combiner

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