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When do contrastive learning signals help spatio-temporal graph forecasting?, , , , , и . SIGSPATIAL/GIS, стр. 5:1-5:12. ACM, (2022)Midas: Microcluster-Based Detector of Anomalies in Edge Streams., , , , и . AAAI, стр. 3242-3249. AAAI Press, (2020)Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification., , и . CinC, стр. 1-4. IEEE, (2021)NodeAug: Semi-Supervised Node Classification with Data Augmentation., , , , , и . KDD, стр. 207-217. ACM, (2020)Identifying through Flows for Recovering Latent Representations., , и . ICLR, OpenReview.net, (2020)Time Series Anomaly Detection With Adversarial Reconstruction Networks., , , , , , и . IEEE Trans. Knowl. Data Eng., 35 (4): 4293-4306 (апреля 2023)LPGNet: Link Private Graph Networks for Node Classification., , , и . CCS, стр. 1813-1827. ACM, (2022)GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs., , , и . ECML/PKDD (2), том 12976 из Lecture Notes in Computer Science, стр. 36-51. Springer, (2021)MStream: Fast Anomaly Detection in Multi-Aspect Streams., , , , и . WWW, стр. 3371-3382. ACM / IW3C2, (2021)DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams., , , и . KDD, стр. 1057-1066. ACM, (2017)