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Publication
ICPR 2012
Conference paper
Hash-based structural similarity for semi-supervised Learning on attribute graphs
Abstract
We present an efficient method to compute similarity between graph nodes by comparing their neighborhood structures rather than proximity. The key is to use a hash for avoiding expensive subgraph comparison. Experiments show that the proposed algorithm performs well in semi-supervised node classification. © 2012 ICPR Org Committee.