Data similarity (or distance) computation is a fundamental research topic which underpins
many high-level applications based on similarity measures in machine learning and data …

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for
network-structured data, aims to predict whether there exists a link between two nodes. The …

In many search applications related to passage retrieval, text entailment, and subgraph
search, the query and each'document'is a set of elements, with a document being relevant if …

Estimating set similarity and detecting highly similar sets are fundamental problems in areas
such as databases, machine learning, and information retrieval. MinHash is a well-known …

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Attributed
network embedding aims to learn a low-dimensional representation for each node of a …

Minwise hashing has become a standard tool to calculate signatures which allow direct
estimation of Jaccard similarities. While very efficient algorithms already exist for the …

As an efficient tool for approximate similarity computation and search, Locality Sensitive
Hashing (LSH) has been widely used in many research areas including databases, data …

Min-Hash, which is widely used for efficiently estimating similarities of bag-of-words
represented data, plays an increasingly important role in the era of big data. It has been …

Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary
sets. Consistent Weighted Sampling (CWS) generalizes the Min-Hash scheme to sketch …

Existing graph classification usually relies on an exhaustive enumeration of substructure
patterns, where the number of substructures expands exponentially wrt with the size of the …