Jiawei Han

TL;DR: A filter-and verification search framework is designed and implemented, which can first generate promising subgraph candidates using off line indices built by data mining results, and then verify candidates with a recursive pruning matching process, and the effectiveness of the query-driven semantic similarity search framework and the efficiency of the proposed methodology on multiple real-world heterogeneous information networks.

TL;DR: This work proposes a new hybrid method that employs both general assumptions on the visual rendering of lists, and the structural representation of items contained in them, and shows that it significantly outperforms existing methods across a varied Web corpus.

TL;DR: A semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data.

TL;DR: In this article, the authors study the compilation and efficient processing of asynchronous chain recursions and show that many complex function-free recursions, which may contain single or multiple linear recursive rules, nonlinear recursive rules and mutually recursive functions, can be compiled to asynchronous chains recursions.

TL;DR: A two-stage approach to find anomalies in complex datasets with high accuracy as well as low time complexity and space cost by employing an efficient deterministic space partition algorithm and generating a small set of anomaly candidates with a single scan of the dataset.