Feb. 12, 2024, 5:43 a.m. | Xi Chen Siwei Zhang Yun Xiong Xixi Wu Jiawei Zhang Xiangguo Sun Yao Zhang Yinglong Zhao

cs.LG updates on arXiv.org arxiv.org

Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems. To facilitate representation learning on TIGs, researchers have proposed a series of TIG models. However, these models are still facing two tough gaps between the pre-training and downstream predictions in their ``pre-train, predict'' training paradigm. First, the temporal discrepancy between the pre-training and inference data severely undermines the models' applicability in distant future predictions on the dynamically evolving data. Second, the semantic divergence between pretext and downstream tasks hinders …

cs.ai cs.lg cs.si graphs paradigm predictions pre-training prompt prompt learning representation representation learning researchers series systems temporal train training world

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