https://github.com/saran-nns/self_organized_criticality
Criticality analysis on complex networks using Power-law scaling functions, exponential relations, and phase transitions
https://github.com/saran-nns/self_organized_criticality
complex-networks complex-system criticality echo-state-networks hebbian-learning hierarchical-temporal-memory liquid-state-machine neuroplasticity reservoir-computing self-organized-criticality self-organizing-network
Last synced: about 1 month ago
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Criticality analysis on complex networks using Power-law scaling functions, exponential relations, and phase transitions
- Host: GitHub
- URL: https://github.com/saran-nns/self_organized_criticality
- Owner: Saran-nns
- License: mit
- Created: 2020-10-11T16:09:05.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-26T09:12:59.000Z (about 1 year ago)
- Last Synced: 2025-03-10T11:19:16.810Z (2 months ago)
- Topics: complex-networks, complex-system, criticality, echo-state-networks, hebbian-learning, hierarchical-temporal-memory, liquid-state-machine, neuroplasticity, reservoir-computing, self-organized-criticality, self-organizing-network
- Language: Python
- Homepage:
- Size: 188 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Self-Organizing Criticality
This repository has methods to investigate criticality in Complex networks. The API is under active development, breaking changes may occur.[](https://travis-ci.org/Saran-nns/self_organized_criticality)
[](https://codecov.io/gh/Saran-nns/self_organized_criticality)
[](https://badge.fury.io/py/complexnetwork)
[](https://github.com/psf/black)

[](https://opensource.org/licenses/MIT)## Installation
```python
pip install complexnetwork
```
to install from source,```python
pip install git+https://github.com/Saran-nns/complexnetwork
```## Workflow
- [x] Avalanche size, interval, duration
- [x] Power laws (Zipf's law) for avalanche size, interval, duration
- [x] Exponent Estimate and Uncertainty
- [x] Ising Model - One and Two point function correlation and covariance
- [x] Plotters
- [ ] Scaling function
- [ ] Exponent Relationships - Avalanche Lifetime Exponent, Size distribution exponent, Height rescaling Exponent
- [ ] Branching Process - Branching Ratio
- [ ] Different Phase Transitions
- [ ] Information transfer - Mutual Information, Transfer Entropy , Partial Information Decomposition, Active information storage, Memory capacity
- [x] PyPi package
- [ ] AutoDoc
- [ ] References