March 14, 2024, 4:43 a.m. | Haoyang Li, Xin Wang, Wenwu Zhu

cs.LG updates on arXiv.org arxiv.org

arXiv:2302.02926v2 Announce Type: replace
Abstract: Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of …

abstract academia arxiv cs.lg curriculum data graph graph data however importance industry literature machine machine learning machine learning models performance random samples survey training type

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