Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity
arXiv: Learning, 2019.
We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGRAPHEMB, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised an...More
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