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Chaos. 2018 Aug;28(8):083106. doi: 10.1063/1.5027007.

The dynamics of knowledge acquisition via self-learning in complex networks.

Author information

1
Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil.
2
São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil.
3
Department of Computer Science, Federal University of São Carlos, São Carlos, São Paulo 13565-905, Brazil.

Abstract

Studies regarding knowledge organization and acquisition are of great importance to understand areas related to science and technology. A common way to model the relationship between different concepts is through complex networks. In such representations, networks' nodes store knowledge and edges represent their relationships. Several studies that considered this type of structure and knowledge acquisition dynamics employed one or more agents to discover node concepts by walking on the network. In this study, we investigate a different type of dynamics adopting a single node as the "network brain." Such a brain represents a range of real systems such as the information about the environment that is acquired by a person and is stored in the brain. To store the discovered information in a specific node, the agents walk on the network and return to the brain. We propose three different dynamics and test them on several network models and on a real system, which is formed by journal articles and their respective citations. The results revealed that, according to the adopted walking models, the efficiency of self-knowledge acquisition has only a weak dependency on topology and search strategy.

PMID:
30180654
DOI:
10.1063/1.5027007

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