TY - GEN

T1 - Randomized Algorithms with Adaptive Tuning of Parameters for Detecting Communities in Graphs

AU - Amelina, Natalia

AU - Granichin, Oleg

AU - Granichina, Olga

AU - Kirianovskii, Ilia

AU - Prodanov, Timofey

PY - 2016

Y1 - 2016

N2 - In the last years, the study of complex networks grows rapidly and search of tightly connected groups of nodes, or community detection, has proved to be a powerful tool for analyzing the real systems. Randomized algorithms are effective for detecting communities but there is no set of optimal parameters that makes these algorithms create a good partitions into communities for every input complex network. In this paper we consider two randomized algorithms and, based on the stochastic approximation, propose two new adaptive modifications that adjust parameters to the input data and create a good partitions for wider range of input networks.

AB - In the last years, the study of complex networks grows rapidly and search of tightly connected groups of nodes, or community detection, has proved to be a powerful tool for analyzing the real systems. Randomized algorithms are effective for detecting communities but there is no set of optimal parameters that makes these algorithms create a good partitions into communities for every input complex network. In this paper we consider two randomized algorithms and, based on the stochastic approximation, propose two new adaptive modifications that adjust parameters to the input data and create a good partitions for wider range of input networks.

KW - STOCHASTIC-APPROXIMATION

U2 - 10.1109/CDC.2016.7799226

DO - 10.1109/CDC.2016.7799226

M3 - статья в сборнике материалов конференции

T3 - IEEE Conference on Decision and Control

SP - 6222

EP - 6227

BT - 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC)

PB - IEEE Canada

Y2 - 12 December 2016 through 14 December 2016

ER -