  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, network's 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 considering a single node as the "network brain".
Such 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. Surprisingly, the results revealed
that, according to the adopted walking models, the efficiency of self-knowledge
acquisition has only a weak dependency on the topology, search strategy and
localization of the network brain.
