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ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation., , , , , , и . WWW, стр. 759-769. ACM, (2023)WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window., , , , , , , и . KDD, стр. 3650-3662. ACM, (2023)Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks., , , , , , , , , и 1 other автор(ы). ACM Trans. Web, 17 (3): 22:1-22:27 (августа 2023)MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems., , , , , , и . KDD, стр. 665-674. ACM, (2021)Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks., , , , , , , , , и . KDD, стр. 1150-1160. ACM, (2021)Graph Random Neural Networks for Semi-Supervised Learning on Graphs., , , , , , , , и . NeurIPS, (2020)Course Concept Extraction in MOOC via Explicit/Implicit Representation., , , и . DSC, стр. 339-345. IEEE, (2018)Understanding Dropouts in MOOCs., , и . AAAI, стр. 517-524. AAAI Press, (2019)XiaoMu: an AI-driven assistant for MOOCs., , , , , , и . Sci. China Inf. Sci., (2021)MOOCCubeX: A Large Knowledge-centered Repository for Adaptive Learning in MOOCs., , , , , , , , , и 9 other автор(ы). CIKM, стр. 4643-4652. ACM, (2021)