Page 431 - 《软件学报》2025年第4期

邹慧琪 等: 基于图神经网络的复杂时空数据挖掘方法综述 1837

urban computing: A survey. IEEE Trans. on Knowledge and Data Engineering, 2023, 36(10): 5388–5408. [doi: 10.1109/TKDE.2023.
3333824]
[4] Liu J, Shang XQ, Song LY, Tan YC. Progress of graph neural networks on complex graph mining. Ruan Jian Xue Bao/Journal of
Software, 2022, 33(10): 3582–3618 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6626.htm [doi: 10.13328/j.cnki.
jos.006626]
[5] Yu B, Yin HT, Zhu ZX. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proc. of
the 27th Int’l Joint Conf. on Artificial Intelligence. Stockholm: ijcai.org, 2018. 3634–3640. [doi: 10.24963/ijcai.2018/505]
[6] Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In: Proc. of
the 30th Int’l Conf. on Neural Information Processing Systems. Barcelona: Curran Associates Inc., 2016. 3844–3852.
[7] Dauphin YN, Fan A, Auli M, Grangier D. Language modeling with gated convolutional networks. In: Proc. of the 34th Int’l Conf. on
Machine Learning. Sydney: JMLR.org, 2017. 933–941.
[8] Qian WZ, Zhang DL, Zhao Y, Zheng K, Yu JJQ. Uncertainty quantification for traffic forecasting: A unified approach. In: Proc. of the
39th IEEE Int’l Conf. on Data Engineering. Anaheim: IEEE, 2023. 992–1004. [doi: 10.1109/ICDE55515.2023. 00081]
[9] Dey R, Salem FM. Gate-variants of gated recurrent unit (GRU) neural networks. In: Proc. of the 60th IEEE Int’l Midwest Symp. on
Circuits and Systems. Boston: IEEE, 2017. 1597–1600. [doi: 10.1109/MWSCAS.2017.8053243]
[10] Bai L, Yao LN, Li C, Wang XZ, Wang C. Adaptive graph convolutional recurrent network for traffic forecasting. In: Proc. of the 34th
Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 17804–17815.
[11] Lu B, Gan XY, Zhang WN, Yao HX, Fu LY, Wang XB. Spatio-temporal graph few-shot learning with cross-city knowledge transfer. In:
Proc. of the 28th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Washington: ACM, 2022. 1162–1172. [doi: 10.1145/
3534678.3539281]
[12] Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. arXiv:1710.10903, 2018.
[13] Guo SN, Lin YF, Feng N, Song C, Wan HY. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting.
In: Proc. of the 33rd AAAI Conf. on Artificial Intelligence. Honolulu: AAAI, 2019. 922–929. [doi: 10.1609/aaai.v33i01.3301922]
[14] Liu J, Li TR, Xie P, Du SD, Teng F, Yang X. Urban big data fusion based on deep learning: An overview. Information Fusion, 2020, 53:
123–133. [doi: 10.1016/j.inffus.2019.06.016]
[15] Jiang WW, Luo JY. Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 2022, 207: 117921. [doi:
10.1016/j.eswa.2022.117921]
[16] Conf. on Machine Learning. New York: JMLR.org, 2016. 1050–1059.
Jin M, Wen QS, Liang YX, Zhang CL, Xue SQ, Wang X, Zhang J, Wang Y, Chen HF, Li XL, Pan SR, Tseng VS, Zheng Y, Chen L,
Xiong H. Large models for time series and spatio-temporal data: A survey and outlook. arXiv:2310.10196, 2023.
[17] Sankar A, Wu YH, Gou L, Zhang W, Yang H. DySAT: Deep neural representation learning on dynamic graphs via self-attention
networks. In: Proc. of the 13th Int’l Conf. on Web Search and Data Mining. Houston: ACM, 2020. 519–527. [doi: 10.1145/3336191.
3371845]
[18] Wu ZH, Pan SR, Long GD, Jiang J, Zhang CQ. Graph WaveNet for deep spatial-temporal graph modeling. In: Proc. of the 28th Int’l
Joint Conf. on Artificial Intelligence. Macao: ijcai.org, 2019. 1907–1913. [doi: 10.24963/ijcai.2019/264]
[19] Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: Proc. of the 5th Int’l Conf. on Learning
Representations. Toulon: OpenReview.net, 2017.
[20] van den Oord A, Dieleman S, Zen HG, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior AW, Kavukcuoglu K. WaveNet: A
generative model for raw audio. In: Proc. of the 9th ISCA Speech Synthesis Workshop. Sunnyvale: ISCA, 2016. 125.
[21] Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: Proc. of the 33rd Int’l

[22] Li YG, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: Proc. of the 6th
Int’l Conf. on Learning Representations. Vancouver: OpenReview.net, 2018.
[23] Huang RZ, Huang CY, Liu YB, Dai GN, Kong WY. LSGCN: Long short-term traffic prediction with graph convolutional networks. In:
Proc. of the 29th Int’l Joint Conf. Artificial Intelligence. Yokohama: ijcai.org, 2020. 2355–2361. [doi: 10.24963/ijcai.2020/326]
[24] Han LZ, Ma XJ, Sun LL, Du BW, Fu YJ, Lv WF, Xiong H. Continuous-time and multi-level graph representation learning for origin-
destination demand prediction. In: Proc. of the 28th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Washington:
ACM, 2022. 516–524. [doi: 10.1145/3534678.3539273]
[25] Xu Y, Han LZ, Zhu TY, Sun LL, Du BW, Lv WF. Continuous-time and discrete-time representation learning for origin-destination
demand prediction. IEEE Trans. on Intelligent Transportation Systems, 2024, 25(3): 2382–2393. [doi: 10.1109/TITS.2023.3323945]
[26] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the