Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks.

ABSTRACT: Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthet

SUBMITTER: Miranda GHB 

PROVIDER: S-EPMC5118793 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature