Under review as a conference paper at ICLR 2019 MILE : A Multi-Level Framework for Scalable Graph Embedding

2018

Subfields: Graph Learning

Citations in the corpus

• Graph Clustering with Graph Neural Networks (2020, background): “As far as we know, all pooling strategies for learning node embeddings without attributes have been axiomatic [10, 14, 33].”
• PyTorch-BigGraph: A Large-scale Graph Embedding System (2019, methodology): “We follow the typical methods (Perozzi et al., 2014; Liang et al., 2018) to evaluate the embedding performance, where we run a 10-fold cross validation by randomly selecting 90% of the labeled data as training data and the rest as testing data.”