Mining Path Association Rules in Large Property Graphs (with Appendix)

TL;DR

This paper introduces path association rule mining (PARM) for large property graphs, developing an efficient algorithm PIONEER that uncovers regular path patterns using scalable techniques validated on real-world data.

Contribution

It extends association rule mining to path patterns in large property graphs and proposes a scalable, efficient algorithm PIONEER with approximation and parallelization.

Findings

PIONEER effectively discovers meaningful path association rules.

The algorithm demonstrates scalability on large real-world graphs.

Experimental results confirm the significance of mined rules.

Abstract

How can we mine frequent path regularities from a graph with edge labels and vertex attributes? The task of association rule mining successfully discovers regular patterns in item sets and substructures. Still, to our best knowledge, this concept has not yet been extended to path patterns in large property graphs. In this paper, we introduce the problem of path association rule mining (PARM). Applied to any \emph{reachability path} between two vertices within a large graph, PARM discovers regular ways in which path patterns, identified by vertex attributes and edge labels, co-occur with each other. We develop an efficient and scalable algorithm PIONEER that exploits an anti-monotonicity property to effectively prune the search space. Further, we devise approximation techniques and employ parallelization to achieve scalable path association rule mining. Our experimental study using real-world graph data verifies the significance of path association rules and the efficiency of our solutions.