Approximating Betweenness Centrality in Fully-dynamic Networks

TL;DR

This paper introduces the first provably approximate algorithms for dynamic betweenness centrality, enabling efficient in-memory computation on large evolving networks with significant speedups and accurate node ranking preservation.

Contribution

It presents novel approximation algorithms with error guarantees for dynamic betweenness, including new bounds on vertex diameter and efficient update methods for shortest paths.

Findings

Achieves up to several orders of magnitude speedup over recomputation.

Maintains high accuracy and node ranking quality in dynamic networks.

Enables in-memory analysis of large networks with millions of edges.

Abstract

Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths. Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed. Besides that, recent years have seen the publication of dynamic algorithms for efficient recomputation of betweenness in networks that change over time. In this paper we propose the first betweenness centrality approximation algorithms with a provable guarantee on the maximum approximation error for dynamic networks. Several new intermediate algorithmic results contribute to the respective approximation algorithms: (i) new upper bounds on the vertex diameter, (ii) the first fully-dynamic algorithm for updating an approximation of the vertex diameter in undirected graphs, and (iii) an algorithm with lower time complexity for updating single-source shortest paths in unweighted graphs after a batch of edge actions. Using approximation, our algorithms are the first to make in-memory computation of betweenness in dynamic networks with millions of edges feasible. Our experiments show that our algorithms can achieve substantial speedups compared to recomputation, up to several orders of magnitude. Moreover, the approximation accuracy is usually significantly better than the theoretical guarantee in terms of absolute error. More importantly, for reasonably small approximation error thresholds, the rank of nodes is well preserved, in particular for nodes with high betweenness.