Efficient Subgraph Similarity Search on Large Probabilistic Graph Databases

TL;DR

This paper addresses the challenge of subgraph similarity search in large probabilistic graphs with correlated edges, proposing a filter-and-verify framework that improves efficiency through probabilistic bounds and sampling, validated by experiments.

Contribution

It introduces a novel filter-and-verify framework for subgraph similarity search on correlated probabilistic graphs, including probabilistic bounds and sampling algorithms, addressing a complex #P-complete problem.

Findings

The proposed method significantly reduces search time.

Probabilistic bounds effectively prune candidate graphs.

Sampling algorithm accurately verifies subgraph similarity.

Abstract

Many studies have been conducted on seeking the efficient solution for subgraph similarity search over certain (deterministic) graphs due to its wide application in many fields, including bioinformatics, social network analysis, and Resource Description Framework (RDF) data management. All these works assume that the underlying data are certain. However, in reality, graphs are often noisy and uncertain due to various factors, such as errors in data extraction, inconsistencies in data integration, and privacy preserving purposes. Therefore, in this paper, we study subgraph similarity search on large probabilistic graph databases. Different from previous works assuming that edges in an uncertain graph are independent of each other, we study the uncertain graphs where edges' occurrences are correlated. We formally prove that subgraph similarity search over probabilistic graphs is #P-complete, thus, we employ a filter-and-verify framework to speed up the search. In the filtering phase,we develop tight lower and upper bounds of subgraph similarity probability based on a probabilistic matrix index, PMI. PMI is composed of discriminative subgraph features associated with tight lower and upper bounds of subgraph isomorphism probability. Based on PMI, we can sort out a large number of probabilistic graphs and maximize the pruning capability. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. The efficiency of our proposed solutions has been verified through extensive experiments.