TY - GEN
T1 - Fast distributed algorithms for testing graph properties
AU - Censor-Hillel, Keren
AU - Fischer, Eldar
AU - Schwartzman, Gregory
AU - Vasudev, Yadu
N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2016.
PY - 2016
Y1 - 2016
N2 - We provide a thorough study of distributed property testing - producing algorithms for the approximation problems of property testing in the CONGEST model. In particular, for the so-called dense graph testing model we emulate sequential tests for nearly all graph properties having 1-sided tests, while in the general and sparse models we obtain faster tests for triangle-freeness, cycle-freeness and bipartiteness, respectively. In addition, we show a logarithmic lower bound for testing bipartiteness and cycle-freeness, which holds even in the LOCAL model. In most cases, aided by parallelism, the distributed algorithms have a much shorter running time as compared to their counterparts from the sequential querying model of traditional property testing. The simplest property testing algorithms allow a relatively smooth transitioning to the distributed model. For the more complex tasks we develop new machinery that may be of independent interest.
AB - We provide a thorough study of distributed property testing - producing algorithms for the approximation problems of property testing in the CONGEST model. In particular, for the so-called dense graph testing model we emulate sequential tests for nearly all graph properties having 1-sided tests, while in the general and sparse models we obtain faster tests for triangle-freeness, cycle-freeness and bipartiteness, respectively. In addition, we show a logarithmic lower bound for testing bipartiteness and cycle-freeness, which holds even in the LOCAL model. In most cases, aided by parallelism, the distributed algorithms have a much shorter running time as compared to their counterparts from the sequential querying model of traditional property testing. The simplest property testing algorithms allow a relatively smooth transitioning to the distributed model. For the more complex tasks we develop new machinery that may be of independent interest.
UR - http://www.scopus.com/inward/record.url?scp=84988640492&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-662-53426-7_4
DO - https://doi.org/10.1007/978-3-662-53426-7_4
M3 - منشور من مؤتمر
SN - 9783662534250
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 43
EP - 56
BT - Distributed Computing - 30th International Symposium, DISC 2016, Proceedings
A2 - Gavoille, Cyril
A2 - Ilcinkas, David
T2 - 30th International Symposium on Distributed Computing, DISC 2016
Y2 - 27 September 2016 through 29 September 2016
ER -