Web: http://arxiv.org/abs/2201.11511

Jan. 28, 2022, 2:11 a.m. | Jianpeng Liao, Qian Tao, Jun Yan

cs.LG updates on arXiv.org arxiv.org

Graph-based semi-supervised learning, which can exploit the connectivity
relationship between labeled and unlabeled data, has been shown to outperform
the state-of-the-art in many artificial intelligence applications. One of the
most challenging problems for graph-based semi-supervised node classification
is how to use the implicit information among various data to improve the
performance of classifying. Traditional studies on graph-based semi-supervised
learning have focused on the pairwise connections among data. However, the data
correlation in real applications could be beyond pairwise and more …

arxiv classification graph graph-based graph neural networks networks neural neural networks

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