# Subsampling scaling: a theory about inference from partly observed systems

## TL;DR

This paper develops a theoretical framework to correct biases caused by spatial subsampling in observations of complex systems, enabling accurate inference of their true properties.

## Contribution

The authors derive a subsampling scaling theory applicable to various observables, allowing correction of biases and analysis of system criticality from partly observed data.

## Key findings

- Power-law scaling observed only in mature neural networks
- Subsampling scaling distinguishes critical from subcritical systems
- Framework applicable to diverse systems like epidemics and neural networks

## Abstract

In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system's aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development.