Feb. 13, 2024, 5:42 a.m. | Kaiwen Dong Haitao Mao Zhichun Guo Nitesh V. Chawla

cs.LG updates on arXiv.org arxiv.org

Link prediction is a crucial task in graph machine learning, where the goal is to infer missing or future links within a graph. Traditional approaches leverage heuristic methods based on widely observed connectivity patterns, offering broad applicability and generalizability without the need for model training. Despite their utility, these methods are limited by their reliance on human-derived heuristics and lack the adaptability of data-driven approaches. Conversely, parametric link predictors excel in automatically learning the connectivity patterns from data and achieving …

connectivity context cs.lg future graph in-context learning link prediction machine machine learning patterns prediction training utility

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