A Faster Algorithm for Fully Dynamic Betweenness Centrality

We present a new fully dynamic algorithm for maintaining betweenness centrality (BC) of vertices in a directed graph $G=(V,E)$ with positive edge weights. BC is a widely used parameter in the analysis of large complex networks. We achieve an amortized $O((\nu^*)^2 \log^2 n)$ time per update, where $n = |V|$ and $\nu^*$ bounds the number of distinct edges that lie on shortest paths through any single vertex. This result improves on the amortized bound for fully dynamic BC in [Pontecorvi-Ramachandran2015] by a logarithmic factor. Our algorithm uses new data structures and techniques that are extensions of the method in the fully dynamic algorithm in Thorup [Thorup2004] for APSP in graphs with unique shortest paths. For graphs with $\nu^* = O(n)$, our algorithm matches the fully dynamic APSP bound in [Thorup2004], which holds for graphs with $\nu^* = n-1$, since it assumes unique shortest paths.Comment: The current revision (v3) includes minor changes in the Introduction. There is no change to the main result. A brief summary of this paper will appear in Proc. ISAAC 2015, in a paper by the authors entitled "Fully Dynamic Betweenness Centrality''. arXiv admin note: text overlap with arXiv:1412.385

Approximating Betweenness Centrality in Fully-dynamic Networks

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.Comment: arXiv admin note: substantial text overlap with arXiv:1504.07091, arXiv:1409.624