论文标题
节点嵌入的图抽样
Graph sampling for node embedding
论文作者
论文摘要
节点嵌入是图表学习中的一个核心主题。对于任何需要全部操作的方法，计算效率和可伸缩性都可能具有挑战性。我们提出了对节点嵌入的采样方法，或者没有特征向量的明确建模，旨在从与图形laplacien相关的特征向量和与图形相关的给定值中提取有用的信息。
Node embedding is a central topic in graph representation learning. Computational efficiency and scalability can be challenging to any method that requires full-graph operations. We propose sampling approaches to node embedding, with or without explicit modelling of the feature vector, which aim to extract useful information from both the eigenvectors related to the graph Laplacien and the given values associated with the graph.
