Hash-based structural similarity for semi-supervised Learning on attribute graphs

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.