Symmetric Continuous Subgraph Matching with Bidirectional Dynamic Programming

TL;DR

This paper introduces SymBi, a novel symmetric algorithm for continuous subgraph matching in dynamic graphs, outperforming existing methods by up to three orders of magnitude through bidirectional dynamic programming.

Contribution

The paper proposes a new symmetric algorithm using a directed acyclic graph for more effective pruning and faster matching in dynamic graphs, improving over TurboFlux.

Findings

SymBi outperforms TurboFlux by up to 1000x in speed.

Extensive experiments validate the efficiency of the proposed method.

The approach effectively handles real and synthetic datasets.

Abstract

In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critical patterns in such dynamic graphs plays an important role in various application domains such as fraud detection, cyber security, and recommendation systems for social networks. Given a dynamic data graph and a query graph, the continuous subgraph matching problem is to find all positive matches for each edge insertion and all negative matches for each edge deletion. The state-of-the-art algorithm TurboFlux uses a spanning tree of a query graph for filtering. However, using the spanning tree may have a low pruning power because it does not take into account all edges of the query graph. In this paper, we present a symmetric and much faster algorithm SymBi which maintains an auxiliary data structure based on a directed acyclic graph instead of a spanning tree, which maintains the intermediate results of bidirectional dynamic programming between the query graph and the dynamic graph. Extensive experiments with real and synthetic datasets show that SymBi outperforms the state-of-the-art algorithm by up to three orders of magnitude in terms of the elapsed time.