# Human information processing in complex networks

**Authors:** Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, and Danielle S., Bassett

arXiv: 1906.00926 · 2020-03-27

## TL;DR

This paper introduces an analytical framework to understand how the structure of complex networks influences human perception and communication efficiency, revealing that hierarchical, heterogeneous, and clustered networks optimize information transfer.

## Contribution

The study develops a new framework linking network topology to perceived information, highlighting the role of hierarchical and clustered structures in efficient human communication.

## Key findings

- Networks with high entropy communicate large amounts of information.
- Efficient communication correlates with hierarchical, heterogeneous, and clustered network features.
- Real-world networks balance high information content with low divergence from human expectations.

## Abstract

Humans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many communication networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.

## Full text

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## Figures

136 figures with captions in the complete paper: https://tomesphere.com/paper/1906.00926/full.md

## References

133 references — full list in the complete paper: https://tomesphere.com/paper/1906.00926/full.md

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Source: https://tomesphere.com/paper/1906.00926