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Algorithm for counting large directed loops (2007)

Abstract
We derive a Belief-Propagation algorithm for counting large loops in a directed network. We evaluate the distribution of the number of small loops in a directed random network with given degree sequence. We apply the algorithm to a few characteristic directed networks of various network sizes and loop structures and compare the algorithm with exhaustive counting results when possible. The algorithm is adequate in estimating loop counts for large directed networks and can be used to compare the loop structure of directed networks and their randomized counterparts.. Comment: (9 pages, 3 figures)

Publication details
Download http://arxiv.org/abs/0709.1446
Repository arXiv (United States)
Keywords Condensed Matter - Disordered Systems and Neural Networks
Type text