Learning Robust Representations with Graph Denoising Policy Network

4 Oct 2019Lu WangWenchao YuWei WangWei ChengWei ZhangHongyuan ZhaXiaofeng HeHaifeng Chen

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction. Existing representation learning methods based on graph neural networks and their variants rely on the aggregation of neighborhood information, which makes it sensitive to noises in the graph... (read more)

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