BatchHL: Answering Distance Queries on Batch-Dynamic Networks at Scale

TL;DR

This paper introduces BatchHL, a scalable framework for efficiently answering distance queries on dynamic graphs by combining offline labelling with online search, supported by theoretical analysis and experiments on real networks.

Contribution

It proposes a novel batch-dynamic algorithm that updates distance labels efficiently for dynamic graphs, balancing offline labelling and online search.

Findings

The algorithms are theoretically sound with proven correctness and minimality.

Experiments show significant efficiency and scalability on 14 real-world networks.

Abstract

Many real-world applications operate on dynamic graphs that undergo rapid changes in their topological structure over time. However, it is challenging to design dynamic algorithms that are capable of supporting such graph changes efficiently. To circumvent the challenge, we propose a batch-dynamic framework for answering distance queries, which combines offline labelling and online searching to leverage the advantages from both sides - accelerating query processing through a partial distance labelling that is of limited size but provides a good approximation to bound online searches. We devise batch-dynamic algorithms to dynamize a distance labelling efficiently in order to reflect batch updates on the underlying graph. In addition to providing theoretical analysis for the correctness, labelling minimality, and computational complexity, we have conducted experiments on 14 real-world networks to empirically verify the efficiency and scalability of the proposed algorithms.