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An Experimental Evaluation of Large Scale GBDT Systems., , , и . Proc. VLDB Endow., 12 (11): 1357-1370 (2019)K-core decomposition on super large graphs with limited resources., , , , , , , и . SAC, стр. 413-422. ACM, (2022)Analyzing Online Transaction Networks with Network Motifs., , , , , , и . KDD, стр. 3098-3106. ACM, (2022)DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions., , , и . SIGMOD Conference, стр. 1363-1376. ACM, (2018)Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning., , , , , , , и . CoRR, (2023)Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent., , , , , , , и . Proc. VLDB Endow., 16 (12): 3781-3794 (2023)P2CG: a privacy preserving collaborative graph neural network training framework., , , , , , , , и . VLDB J., 32 (4): 717-736 (июля 2023)KVSAgg: Secure Aggregation of Distributed Key-Value Sets., , , , , , , , и . ICDE, стр. 1775-1789. IEEE, (2023)SKCompress: compressing sparse and nonuniform gradient in distributed machine learning., , , , и . VLDB J., 29 (5): 945-972 (2020)SketchML: Accelerating Distributed Machine Learning with Data Sketches., , , и . SIGMOD Conference, стр. 1269-1284. ACM, (2018)