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Metrics for set similarity are a core aspect of several data mining tasks. Toremove duplicate results in a Web search, for example, a common approach looksat the Jaccard index between all pairs of pages. In social network analysis, amuch-celebrated metric is the Adamic-Adar index, widely used to compare nodeneighborhood sets in the important problem of predicting links. However, withthe increasing amount of data to be processed, calculating the exact similaritybetween all pairs can be intractable. The challenge of working at this scalehas motivated research into efficient estimators for set similarity metrics.The two most popular estimators, MinHash and SimHash, are indeed used inapplications such as document deduplication and recommender systems where largevolumes of data need to be processed. Given the importance of these tasks, thedemand for advancing estimators is evident. We propose DotHash, an unbiasedestimator for the intersection size of two sets. DotHash can be used toestimate the Jaccard index and, to the best of our knowledge, is the firstmethod that can also estimate the Adamic-Adar index and a family of relatedmetrics. We formally define this family of metrics, provide theoretical boundson the probability of estimate errors, and analyze its empirical performance.Our experimental results indicate that DotHash is more accurate than the otherestimators in link prediction and detecting duplicate documents with the samecomplexity and similar comparison time.

DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication