# Knowledge Acquisition: A Complex Networks Approach

## TL;DR

This paper models knowledge acquisition as a multi-agent random walk on complex networks, incorporating influence and memory, and evaluates how different dynamics affect discovery efficiency across various network regions.

## Contribution

It introduces a novel multi-agent random walk model combining true self-avoiding walks and Levy flights influenced by other agents, advancing understanding of knowledge discovery processes.

## Key findings

- Most dynamics parameters do not significantly affect discovery.
- Network core regions facilitate more effective knowledge acquisition.
- Local network structure influences the performance of exploration dynamics.

## Abstract

Complex networks have been found to provide a good representation of the structure of knowledge, as understood in terms of discoverable concepts and their relationships. In this context, the discovery process can be modeled as agents walking in a knowledge space. Recent studies proposed more realistic dynamics, including the possibility of agents being influenced by others with higher visibility or by their own memory. However, rather than dealing with these two concepts separately, as previously approached, in this study we propose a multi-agent random walk model for knowledge acquisition that incorporates both concepts. More specifically, we employed the true self avoiding walk alongside a new dynamics based on jumps, in which agents are attracted by the influence of others. That was achieved by using a L\'evy flight influenced by a field of attraction emanating from the agents. In order to evaluate our approach, we use a set of network models and two real networks, one generated from Wikipedia and another from the Web of Science. The results were analyzed globally and by regions. In the global analysis, we found that most of the dynamics parameters do not significantly affect the discovery dynamics. The local analysis revealed a substantial difference of performance depending on the network regions where the dynamics are occurring. In particular, the dynamics at the core of networks tend to be more effective. The choice of the dynamics parameters also had no significant impact to the acquisition performance for the considered knowledge networks, even at the local scale.