RAQ: Relationship-Aware Graph Querying in Large Networks

TL;DR

RAQ introduces a relationship-aware graph querying method that considers interactions between node and edge labels, improving match relevance in large networks, validated by user preference and experiments.

Contribution

The paper presents RAQ, a novel graph querying approach that incorporates relationship information, addressing a gap in traditional structural similarity methods.

Findings

86% of users preferred relationship-aware matches in IMDb.

RAQ is effective, efficient, and scalable on real-world datasets.

RAQ outperforms traditional methods by considering relationships in graph matching.

Abstract

The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query graph in a (large) target graph. However, almost all existing techniques have tacitly ignored the presence of relationships in graphs, which are usually encoded through interactions between node and edge labels. In this paper, we propose RAQ -- Relationship-Aware Graph Querying, to mitigate this gap. Given a query graph, RAQ identifies the $k$ best matching subgraphs of the target graph that encode similar relationships as in the query graph. To assess the utility of RAQ as a graph querying paradigm for knowledge discovery and exploration tasks, we perform a user survey on the Internet Movie Database (IMDb), where an overwhelming 86% of the 170 surveyed users preferred the relationship-aware match over traditional graph querying. The need to perform subgraph isomorphism renders RAQ NP-hard. The querying is made practical through beam stack search. Extensive experiments on multiple real-world graph datasets demonstrate RAQ to be effective, efficient, and scalable.