Uncertain Graph Sparsification

TL;DR

This paper introduces novel sparsification techniques for uncertain graphs that reduce size and preserve structure, enabling efficient and accurate query processing and analysis.

Contribution

First sparsification methods specifically designed for uncertain graphs, redistributing edge probabilities to maintain structural properties.

Findings

Effective reduction in graph size while preserving key properties

Accurate approximation of queries like PageRank and shortest paths

Demonstrated improvements on real and synthetic datasets

Abstract

Uncertain graphs are prevalent in several applications including communications systems, biological databases and social networks. The ever increasing size of the underlying data renders both graph storage and query processing extremely expensive. Sparsification has often been used to reduce the size of deterministic graphs by maintaining only the important edges. However, adaptation of deterministic sparsification methods fails in the uncertain setting. To overcome this problem, we introduce the first sparsification techniques aimed explicitly at uncertain graphs. The proposed methods reduce the number of edges and redistribute their probabilities in order to decrease the graph size, while preserving its underlying structure. The resulting graph can be used to efficiently and accurately approximate any query and mining tasks on the original graph. An extensive experimental evaluation with real and synthetic datasets illustrates the effectiveness of our techniques on several common graph tasks, including clustering coefficient, page rank, reliability and shortest path distance.