Name
Abstract | ||
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We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest.
The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E|1/2+o(1) with high probability, obtaining a significant speedup with respect to the Θ(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well.
The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/3284359 | Journal of Experimental Algorithmics |
Keywords | DocType | Volume |
Betweenness centrality,graph mining,network analysis,sampling,shortest path algorithm | Journal | 24 |
Issue | ISSN | Citations |
1 | 1084-6654 | 7 |
PageRank | References | Authors |
0.50 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Borassi | 1 | 40 | 4.13 |
| Emanuele Natale | 2 | 74 | 14.52 |