External-Memory Network Analysis Algorithms for Naturally Sparse Graphs
Data Structures and Algorithms
2011-07-01 v1
Abstract
In this paper, we present a number of network-analysis algorithms in the external-memory model. We focus on methods for large naturally sparse graphs, that is, n-vertex graphs that have O(n) edges and are structured so that this sparsity property holds for any subgraph of such a graph. We give efficient external-memory algorithms for the following problems for such graphs: - Finding an approximate d-degeneracy ordering; - Finding a cycle of length exactly c; - Enumerating all maximal cliques. Such problems are of interest, for example, in the analysis of social networks, where they are used to study network cohesion.
Cite
@article{arxiv.1106.6336,
title = {External-Memory Network Analysis Algorithms for Naturally Sparse Graphs},
author = {Michael T. Goodrich and Pawel Pszona},
journal= {arXiv preprint arXiv:1106.6336},
year = {2011}
}
Comments
23 pages, 2 figures. To appear at the 19th Annual European Symposium on Algorithms (ESA 2011)