Name
Abstract | ||
|---|---|---|
Finding central nodes is a fundamental problem in network analysis. Betweenness centrality is a well-known measure which quantifies the importance of a node based on the fraction of shortest paths going though it. Due to the dynamic nature of many today’s networks, algorithms that quickly update centrality scores have become a necessity. For betweenness, several dynamic algorithms have been proposed over the years, targeting different update types (incremental- and decremental-only, fully-dynamic). In this paper we introduce a new dynamic algorithm for updating betweenness centrality after an edge insertion or an edge weight decrease. Our method is a combination of two independent contributions: a faster algorithm for updating pairwise distances as well as number of shortest paths, and a faster algorithm for updating dependencies. Whereas the worst-case running time of our algorithm is the same as recomputation, our techniques considerably reduce the number of operations performed by existing dynamic betweenness algorithms. Our experimental evaluation on a variety of real-world networks reveals that our approach is significantly faster than the current state-of-the-art dynamic algorithms, approximately by one order of magnitude on average. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4230/LIPIcs.SEA.2017.23 | symposium on experimental and efficient algorithms |
DocType | Volume | Citations |
Conference | abs/1704.08592 | 1 |
PageRank | References | Authors |
0.35 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabetta Bergamini | 1 | 49 | 4.82 |
| Henning Meyerhenke | 2 | 522 | 42.22 |
| Mark Ortmann | 3 | 47 | 3.40 |
| Arie Slobbe | 4 | 1 | 0.35 |