Web: http://arxiv.org/abs/2205.05964

May 13, 2022, 1:11 a.m. | Qianggang Ding, Deheng Ye, Tingyang Xu, Peilin Zhao

cs.LG updates on arXiv.org arxiv.org

Graph neural networks (GNNs) have been applied into a variety of graph tasks.
Most existing work of GNNs is based on the assumption that the given graph data
is optimal, while it is inevitable that there exists missing or incomplete
edges in the graph data for training, leading to degraded performance. In this
paper, we propose Generative Predictive Network (GPN), a GNN-based joint
learning framework that simultaneously learns the graph structure and the
downstream task. Specifically, we develop a bilevel …

arxiv framework graph graph neural networks learning networks neural neural networks

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