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Decentralized cluster detection in distributed systems based on self-organized synchronization

: Singh, V.; Esch, M.; Scholtes, I.


Cabri, G. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
SASO 2016, IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems. Proceedings : 12-16 September 2016, Augsburg, Germany
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2016
ISBN: 978-1-5090-3534-2
ISBN: 978-1-5090-3535-9
International Conference on Self-Adaptive and Self-Organizing Systems (SASO) <10, 2016, Augsburg>
Conference Paper
Fraunhofer FKIE ()

In this work, we propose a method for the decentralized detection of clusters, or communities, in large-scale networked systems. Different from other approaches that require global knowledge of the network topology, the proposed method is based on a fully decentralized protocol and allows a node to infer knowledge about the community memberships of its nearest neighbours. It relies on the fact that topological characteristics of a network leave traces in the evolution of a self-organized synchronization process. The preliminary results presented in this report show a promising detection accuracy and justify a further investigation of our approach.