TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database

TL;DR

This paper introduces Ted, a framework for discovering top-k edge-diversified patterns in graph databases, effectively retrieving diverse subgraphs that cover maximum edges, outperforming traditional methods.

Contribution

It formulates the novel Top-k Edge-Diversified Patterns Discovery problem and proposes Ted, an extensible framework with approximation guarantees and optimization strategies.

Findings

Ted outperforms traditional techniques on real-world datasets.

The framework guarantees an approximation ratio to the optimal.

Optimization strategies improve processing performance.

Abstract

With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (e.g., chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose the Top-k Edge-Diversified Patterns Discovery problem to retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called Ted which achieves a guaranteed approximation ratio to the optimal result. Two optimization strategies are further developed to improve the performance. Experimental studies on real-world datasets demonstrate the superiority of Ted to traditional techniques.