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邹慧琪 等: 基于图神经网络的复杂时空数据挖掘方法综述                                                     1841


                      recurrent neural network. Neurocomputing, 2023, 531: 151–162. [doi: 10.1016/j.neucom.2023.02.017]
                 [95]  Chen YK, Li ZH, Yang C, Wang XZ, Long GD, Xu GD. Adaptive graph recurrent network for multivariate time series imputation. In:
                      Proc. of the 29th Int’l Conf. on Neural Information Processing. Singapore: Springer, 2022. 64–73. [doi: 10.1007/978-981-99-1642-9_6]
                 [96]  Kong  XJ,  Zhou  WF,  Shen  GJ,  Zhang  WY,  Liu  NL,  Yang  Y.  Dynamic  graph  convolutional  recurrent  imputation  network  for
                      spatiotemporal traffic missing data. Knowledge-based Systems, 2023, 261: 110188. [doi: 10.1016/j.knosys.2022.110188]
                 [97]  Wu XS, Xu MY, Fang J, Wu XW. A multi-attention tensor completion network for spatiotemporal traffic data imputation. IEEE Internet
                      of Things Journal, 2022, 9(20): 20203–20213. [doi: 10.1109/JIOT.2022.3171780]
                 [98]  Sun MJ, Zhou PY, Tian H, Liao Y, Xie HY. Spatial-temporal attention network for crime prediction with adaptive graph learning. In:
                      Proc. of the 31st Int’l Conf. on Artificial Neural Networks. Bristol: Springer, 2022. 656–669. [doi: 10.1007/978-3-031-15931-2_54]
                 [99]  Xia LH, Huang C, Xu Y, Dai P, Bo LF, Zhang XY, Chen TY. Spatial-temporal sequential hypergraph network for crime prediction with
                      dynamic  multiplex  relation  learning.  In:  Proc.  of  the  30th  Int’l  Joint  Conf.  on  Artificial  Intelligence.  Montreal:  ijcai.org,  2021.
                      1631–1637. [doi: 10.24963/ijcai.2021/225]
                 [100]  Panagopoulos G, Nikolentzos G, Vazirgiannis M. Transfer graph neural networks for pandemic forecasting. In: Proc. of the 35th AAAI
                      Conf. on Artificial Intelligence. AAAI, 2021. 4838–4845. [doi: 10.1609/aaai.v35i6.16616]
                 [101]  Jin RD, Xia TQ, Liu X, Murata T, Kim KS. Predicting emergency medical service demand with bipartite graph convolutional networks.

                      IEEE Access, 2021, 9: 9903–9915. [doi: 10.1109/ACCESS.2021.3050607]
                 [102]  Liang YX, Xia YT, Ke SY, Wang YW, Wen QS, Zhang JB, Zheng Y, Zimmermann R. AirFormer: Predicting nationwide air quality in
                      China with Transformers. In: Proc. of the 37th AAAI Conf. on Artificial Intelligence. Washington: AAAI, 2023. 14329–14337. [doi: 10.
                      1609/aaai.v37i12.26676]
                 [103]  Chen SY, Zwart JA, Jia XW. Physics-guided graph meta learning for predicting water temperature and streamflow in stream networks.
                      In: Proc. of the 28th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Washington: ACM, 2022. 2752–2761. [doi: 10.
                      1145/3534678.3539115]
                 [104]  Khodayar M, Liu GY, Wang JH, Kaynak O, Khodayar ME. Spatiotemporal behind-the-meter load and PV power forecasting via deep
                      graph dictionary learning. IEEE Trans. on Neural Networks and Learning Systems, 2021, 32(10): 4713–4727. [doi: 10.1109/TNNLS.
                      2020.3042434]
                 [105]  Karimi AM, Wu YH, Koyuturk M, French RH. Spatiotemporal graph neural network for performance prediction of photovoltaic power
                      systems. In: Proc. of the 35th AAAI Conf. on Artificial Intelligence. AAAI, 2021. 15323–15330. [doi: 10.1609/aaai.v35i17.17799]
                 [106]  Yan JD, Chen YZ, Xiao ZX, Zhang S, Jiang MX, Wang TQ, Zhang T, Lv JL, Becker B, Zhang R, Zhu DJ, Han JW, Yao DZ, Kendrick
                      KM, Liu TM, Jiang X. Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph
                      neural networks (Multi-Head GAGNNs). Medical Image Analysis, 2022, 80: 102518. [doi: 10.1016/j.media.2022.102518]
                 [107]  Qiu  WY,  Ma  L,  Jiang  TZ,  Zhang  Y.  Unrevealing  reliable  cortical  parcellation  of  individual  brains  using  resting-state  functional
                      magnetic resonance imaging and masked graph convolutions. Frontiers in Neuroscience, 2022, 16: 838347. [doi: 10.3389/fnins.2022.
                      838347]
                 [108]  Kim BH, Ye JC, Kim JJ. Learning dynamic graph representation of brain connectome with spatio-temporal attention. In: Proc. of the
                      35th Conf. on Neural Information Processing Systems. NeurIPS, 2021. 4314–4327.
                 [109]  Yang HZ, Li XX, Wu YF, Li SY, Lu S, Duncan JS, Gee JC, Gu S. Interpretable multimodality embedding of cerebral cortex using
                      attention graph network for identifying bipolar disorder. In: Proc. of the 22nd Int’l Conf. on Medical Image Computing and Computer-
                      assisted Intervention. Shenzhen: Springer, 2019. 799–807. [doi: 10.1007/978-3-030-32248-9_89]
                 [110]  Li ML, Chen HB, Cheng ZX. An attention-guided spatiotemporal graph convolutional network for sleep stage classification. Life, 2022,
                      12(5): 622. [doi: 10.3390/life12050622]
                 [111]  Stankevičiūtė K, Azevedo T, Campbell A, Bethlehem R, Liò P. Population graph GNNs for brain age prediction. In: Proc. of the 2020
                      ICML Workshop on Graph Representation Learning and Beyond. 2020. 202.
                 [112]  Liu  YX,  Zheng  YZ,  Zhang  DK,  Lee  VCS,  Pan  SR.  Beyond  smoothing:  Unsupervised  graph  representation  learning  with  edge
                      heterophily discriminating. In: Proc. of the 37th AAAI Conf. on Artificial Intelligence. Washington: AAAI, 2023. 4516–4524. [doi: 10.
                      1609/aaai.v37i4.25573]
                 [113]  Duan JC, Wang SW, Zhang P, Zhu E, Hu JT, Jin H, Liu Y, Dong ZB. Graph anomaly detection via multi-scale contrastive learning
                      networks with augmented view. In: Proc. of the 37th AAAI Conf. on Artificial Intelligence. Washington: AAAI, 2023. 7459–7467. [doi:
                      10.1609/aaai.v37i6.25907]
                 [114]  Chen  JL,  Kou  G.  Attribute  and  structure  preserving  graph  contrastive  learning.  In:  Proc.  of  the  37th  AAAI  Conf.  on  Artificial
                      Intelligence. Washington: AAAI, 2023. 7024–7032. [doi: 10.1609/aaai.v37i6.25858]
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