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GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks., , , and . KDD, page 3525-3536. ACM, (2023)Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling., , , , and . NeurIPS, page 3842-3851. (2019)NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification., , , , and . NeurIPS, (2022)Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs., , , and . ICLR, OpenReview.net, (2023)EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction., , , , and . ICDM, page 1296-1301. IEEE Computer Society, (2018)Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation., , , and . KDD, page 824-834. ACM, (2022)Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach., , and . NeurIPS, page 19435-19447. (2021)Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach., , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 11329-11339. PMLR, (2021)Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach., , , , , and . IJCAI, page 2355-2361. ijcai.org, (2022)Graph Out-of-Distribution Generalization via Causal Intervention., , , , and . WWW, page 850-860. ACM, (2024)