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The dynamics of knowledge acquisition via self-learning in complex networks.
Thales S Lima1, Henrique F de Arruda1, Filipi N Silva2
1Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil.
Chaos (Woodbury, N.Y.)
|September 6, 2018
Summary
This study introduces a "network brain" concept for knowledge acquisition, modeling how information is stored and retrieved. Findings show network structure and search strategies have minimal impact on self-knowledge acquisition efficiency.
Area of Science:
- Knowledge organization and acquisition
- Complex networks in science and technology
Background:
- Complex networks model relationships between concepts, with nodes storing knowledge and edges representing connections.
- Previous studies used agents to discover node concepts via network walks for knowledge acquisition dynamics.
Purpose of the Study:
- To investigate a novel knowledge acquisition dynamic using a single node as a "network brain."
- To model real-world systems where information is acquired and stored, akin to human learning.
Main Methods:
- Proposed three distinct dynamics for the "network brain" model.
- Tested these dynamics on various network models and a real-world citation network of journal articles.
Main Results:
- The efficiency of self-knowledge acquisition demonstrated a weak dependency on network topology.
- The chosen walking models and search strategies had a limited impact on acquisition efficiency.
Conclusions:
- The "network brain" model offers a new perspective on information storage and retrieval dynamics.
- Network structure and search strategy are less critical to self-knowledge acquisition than initially presumed.
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