文献类型：期刊文献

英文题名：T-GAN: A deep learning framework for prediction of temporal complex networks with adaptive graph convolution and attention mechanism

作者：Huang, Ru[1];Ma, Lei[1];He, Jianhua[2];Chu, Xiaoli[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England;[3]Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England

年份：2021

卷号：68

外文期刊名：DISPLAYS

收录：;EI(收录号：20212210429900);WOS:【SCI-EXPANDED(收录号:WOS:000659484500006)】；

基金：This work was supported by National Natural Science Foundation of China, grant number 61673178 and 61922063, Natural Science Foundation of Shanghai, No.20ZR1413800. Our work has also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No 824019 (project COSAFE) and the Marie SklodowskaCurie grant agreement No 101022280 (project VESAFE) . The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that help improve the quality of this paper.

语种：英文

外文关键词：Complex networks; Temporal graph embedding; Graph data mining; Graph neural network

摘要：Complex network is graph network with non-trivial topological features often occurring in real systems, such as video monitoring networks, social networks and sensor networks. While there is growing research study on complex networks, the main focus has been on the analysis and modeling of large networks with static topology. Predicting and control of temporal complex networks with evolving patterns are urgently needed but have been rarely studied. In view of the research gaps we are motivated to propose a novel end-to-end deep learning based network model, which is called temporal graph convolution and attention (T-GAN) for prediction of temporal complex networks. To joint extract both spatial and temporal features of complex networks, we design new adaptive graph convolution and integrate it with Long Short-Term Memory (LSTM) cells. An encoder-decoder framework is applied to achieve the objectives of predicting properties and trends of complex networks. And we proposed a dual attention block to improve the sensitivity of the model to different time slices. Our proposed T-GAN architecture is general and scalable, which can be used for a wide range of real applications. We demonstrate the applications of T-GAN to three prediction tasks for evolving complex networks, namely, node classification, feature forecasting and topology prediction over 6 open datasets. Our T-GAN based approach significantly outperforms the existing models, achieving improvement of more than 4.7% in recall and 25.1% in precision. Additional experiments are also conducted to show the generalization of the proposed model on learning the characteristic of time-series images. Extensive experiments demonstrate the effectiveness of T-GAN in learning spatial and temporal feature and predicting properties for complex networks.