AI Article Synopsis
- The study explores knowledge organization and acquisition through complex networks, with nodes representing knowledge and edges as relationships.
- Researchers investigate a unique dynamic using a single node as a "network brain" to simulate how information is collected and stored, akin to human cognition.
- Three dynamics are proposed and tested on various network models and a real-world system of journal articles and citations, revealing that the efficiency of knowledge acquisition is only slightly influenced by network topology and search strategies.
Article 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.
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| http://dx.doi.org/10.1063/1.5027007 | DOI Listing |
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