Evaluating Top-k Meta Path Queries on Large Heterogeneous Information Networks

Heterogeneous information networks (HINs), which are typed graphs with labeled nodes and edges, have attracted tremendous interest from academia and industry. Given two HIN nodes s and t, and a natural number k, we study the discovery of the k most important paths in real time. The paths found can be used to support friend search, product recommendation, anomaly detection, and graph clustering. Although related algorithms have been proposed before, they were primarily designed to return the k shortest paths from unlabeled graphs. This leads to two problems: (1) there are often many shortest paths between s and t, and so it is not easy to choose the k best ones; and (2) it is arguable whether a shorter path implies a more crucial one. To address these issues, we study the top-k meta path query for a HIN. A meta path abstracts multiple path instances into a high-level path pattern, thereby giving more insight between two nodes. We further study several ranking functions that evaluate the importance of meta paths based on frequency and rarity, rather than on path length. We propose a solution that seamlessly integrates these functions into an A* search framework. The connectivity experiment on ACM dataset shows that our proposed method outperforms state-of-the-art algorithms.