The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks

TL;DR

This paper explores how a single node acting as a 'network brain' acquires knowledge through agent walks in complex networks, revealing weak dependency on network topology and search strategies.

Contribution

It introduces a novel model of knowledge acquisition with a single 'network brain' node and analyzes its dynamics across various network types and real citation data.

Findings

Knowledge acquisition efficiency weakly depends on network topology.

Different walking models show similar results across network types.

The 'network brain' concept models real systems like human cognition.

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, 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.