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@inproceedings{1783802,
author = {Beneš, Nikola and Brim, Luboš and Pastva, Samuel and Šafránek, David},
address = {Neuveden},
booktitle = {Computer Aided Verification - 33rd International Conference, CAV 2021},
doi = {http://dx.doi.org/10.1007/978-3-030-81685-8_24},
editor = {Alexandra Silva, K. Rustan, M. Leino},
keywords = {Bottom SCC; Symbolic algorithm; Boolean network},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Neuveden},
isbn = {978-3-030-81684-1},
pages = {505-528},
publisher = {Springer Nature},
title = {Computing Bottom SCCs Symbolically Using Transition Guided Reduction},
url = {https://link.springer.com/chapter/10.1007/978-3-030-81685-8_24},
year = {2021}
}
TY - JOUR
ID - 1783802
AU - Beneš, Nikola - Brim, Luboš - Pastva, Samuel - Šafránek, David
PY - 2021
TI - Computing Bottom SCCs Symbolically Using Transition Guided Reduction
PB - Springer Nature
CY - Neuveden
SN - 9783030816841
KW - Bottom SCC
KW - Symbolic algorithm
KW - Boolean network
UR - https://link.springer.com/chapter/10.1007/978-3-030-81685-8_24
N2 - Detection of bottom strongly connected components (BSCC) in state-transition graphs is an important problem with many applications, such as detecting recurrent states in Markov chains or attractors in dynamical systems. However, these graphs’ size is often entirely out of reach for algorithms using explicit state-space exploration, necessitating alternative approaches such as the symbolic one. Symbolic methods for BSCC detection often show impressive performance, but can sometimes take a long time to converge in large graphs. In this paper, we provide a symbolic state-space reduction method for labelled transition systems, called interleaved transition guided reduction (ITGR), which aims to alleviate current problems of BSCC detection by efficiently identifying large portions of the non-BSCC states. We evaluate the suggested heuristic on an extensive collection of 125 real-world biologically motivated systems. We show that ITGR can easily handle all these models while being either the only method to finish, or providing at least an order-of-magnitude speedup over existing state-of-the-art methods. We then use a set of synthetic benchmarks to demonstrate that the technique also consistently scales to graphs with more than 2^1000 vertices, which was not possible using previous methods.
ER -
BENEŠ, Nikola, Luboš BRIM, Samuel PASTVA and David ŠAFRÁNEK. Computing Bottom SCCs Symbolically Using Transition Guided Reduction. Online. In Alexandra Silva, K. Rustan, M. Leino. \textit{Computer Aided Verification - 33rd International Conference, CAV 2021}. Neuveden: Springer Nature, 2021, p.~505-528. ISBN~978-3-030-81684-1. Available from: https://dx.doi.org/10.1007/978-3-030-81685-8\_{}24.
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