The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration

ICLR 2019 Yihan GaoChao ZhangJian PengAditya Parameswaran

Learning distributed representations for nodes in graphs is a crucial primitive in network analysis with a wide spectrum of applications. Linear graph embedding methods learn such representations by optimizing the likelihood of both positive and negative edges while constraining the dimension of the embedding vectors... (read more)

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