A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks
Port network information security has received extensive attention in recent years, in which the prediction of node links in the network is significant. A Port network is a dynamic network, and its structure changes continuously with time. Therefore, to effectively utilize the information of the dyn...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10943113/ |
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| author | Zhixin Xia Zhangqi Zheng Feiyang Wei Yongshan Liu Lu Yu |
| author_facet | Zhixin Xia Zhangqi Zheng Feiyang Wei Yongshan Liu Lu Yu |
| author_sort | Zhixin Xia |
| collection | DOAJ |
| description | Port network information security has received extensive attention in recent years, in which the prediction of node links in the network is significant. A Port network is a dynamic network, and its structure changes continuously with time. Therefore, to effectively utilize the information of the dynamic network and improve the prediction efficiency as well as the prediction accuracy, this paper proposes a spatio-temporal tensor graph neural network model, which learns graph structural features from both spatial and temporal aspects to capture the evolution of the dynamic network. Firstly, the model extracts the corresponding spatial features for the port network node links using the graph compression technique and updates the spatial features based on GraphSAGE by sampling and aggregating the neighbor node features. Secondly, for the port network temporal links, this paper introduces an exponential decay model to extract the corresponding temporal features and obtains the temporal edge embedding vectors by convolution method based on the GCN model. The updated spatial features and temporal edge embedding vectors are then weighted for feature fusion to predict the security of port network links. Finally, this paper conducts experiments on seven publicly available dynamic graph datasets, and the results show that the prediction accuracy of the spatio-temporal tensor graph neural network model is better than the baseline models, such as GC-LSTM, HTGN, and GAT, in general. |
| format | Article |
| id | doaj-art-77a97bf17b4846f0873ba4f12ea62343 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-77a97bf17b4846f0873ba4f12ea623432025-08-20T03:06:43ZengIEEEIEEE Access2169-35362025-01-0113616756168410.1109/ACCESS.2025.355526410943113A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port NetworksZhixin Xia0Zhangqi Zheng1Feiyang Wei2Yongshan Liu3https://orcid.org/0000-0002-6891-340XLu Yu4School of Information Science and Engineering, Yanshan University, Qinhuangdao, ChinaSchool of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao, ChinaHebei Port Group Datalink Technology (Xiongan) Company Ltd., Baoding, ChinaPort network information security has received extensive attention in recent years, in which the prediction of node links in the network is significant. A Port network is a dynamic network, and its structure changes continuously with time. Therefore, to effectively utilize the information of the dynamic network and improve the prediction efficiency as well as the prediction accuracy, this paper proposes a spatio-temporal tensor graph neural network model, which learns graph structural features from both spatial and temporal aspects to capture the evolution of the dynamic network. Firstly, the model extracts the corresponding spatial features for the port network node links using the graph compression technique and updates the spatial features based on GraphSAGE by sampling and aggregating the neighbor node features. Secondly, for the port network temporal links, this paper introduces an exponential decay model to extract the corresponding temporal features and obtains the temporal edge embedding vectors by convolution method based on the GCN model. The updated spatial features and temporal edge embedding vectors are then weighted for feature fusion to predict the security of port network links. Finally, this paper conducts experiments on seven publicly available dynamic graph datasets, and the results show that the prediction accuracy of the spatio-temporal tensor graph neural network model is better than the baseline models, such as GC-LSTM, HTGN, and GAT, in general.https://ieeexplore.ieee.org/document/10943113/Port networkdynamic networkspatio-temporal tensor graph neural networkexponential decay modelGCNlink prediction |
| spellingShingle | Zhixin Xia Zhangqi Zheng Feiyang Wei Yongshan Liu Lu Yu A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks IEEE Access Port network dynamic network spatio-temporal tensor graph neural network exponential decay model GCN link prediction |
| title | A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks |
| title_full | A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks |
| title_fullStr | A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks |
| title_full_unstemmed | A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks |
| title_short | A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks |
| title_sort | spatio temporal tensor graph neural network based method for node link prediction in port networks |
| topic | Port network dynamic network spatio-temporal tensor graph neural network exponential decay model GCN link prediction |
| url | https://ieeexplore.ieee.org/document/10943113/ |
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