Efficient Subgraph Matching on Billion Node Graphs

TL;DR

This paper introduces a scalable distributed algorithm for subgraph matching on billion-node graphs, leveraging parallel computing and exploration techniques to overcome the limitations of traditional super-linear indexing methods.

Contribution

The paper presents a novel distributed algorithm that enables efficient subgraph matching on large-scale graphs without relying on super-linear indices.

Findings

Feasibility of subgraph matching on web-scale graphs demonstrated

Algorithm achieves efficient query processing with parallel exploration

Supports billion-node graphs in distributed memory environments

Abstract

The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.