Python code for calculating effective information in networks. This can then be used to search for macroscale representations of a network such that the coarse grained representation has more effective information than the microscale, a phenomenon known as causal emergence. This code accompanies the recent paper:

Uncertainty and causal emergence in complex networks
Brennan Klein and Erik Hoel, 2019.
arXiv:1907.03902

Fig. 1: Effective information vs network size.

Fig. 2: Causal emergence vs preferential attachment.

1. Chapter 01 - Network Effective Information
2. Chapter 02 - Network Size and Effective Information
3. Chapter 03 - Determinism and Degeneracy
4. Chapter 04 - Effective Information in Real Networks
5. Chapter 05 - Causal Emergence in Preferential Attachment and SBMs
6. Chapter 06 - Causal Emergence and the Emergence of Scale
7. Chapter 07 - Estimating Causal Emergence in Real Networks
8. Chapter 08 - Miscellaneous

In order to use this code, first clone/download the repository. Below is a simple example usage. Please feel free to reach out if you find any bugs, have any questions, or if for some reason the code does not run.

>>> from ei_net import *
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> print("effective_information(G) =", effective_information(G))

EI(G) = 2.3500950888734686

The tutorial notebooks are designed to walk through some of the main results from the paper above, in addition to several in-depth analyses that were not included in the original paper.

This code is written in Python 3.x and uses the following packages:

• NetworkX
• Scipy
• Numpy
• And for replicating figures, you will need:
  • matplotlib
  • Pandas

The colormaps in the paper are from https://matplotlib.org/cmocean/ and the named colors are from https://medialab.github.io/iwanthue/.

If you use these methods and this code in your own research, please cite our paper:

Klein, B. & Hoel, E. (2019). Uncertainty and causal emergence in complex networks. arXiv:1907.03902

Bibtex:

@article{klein2019causalemergence,
  title={Uncertainty and causal emergence in complex networks},
  author={Klein, Brennan and Hoel, Erik},
  journal={arXiv preprint arXiv:1907.03902},
  year={2019}
}

• Hoel, E. (2017). When the map is better than the territory. Entropy. 19(5), 188; doi: 10.3390/e19050188.
  • recent work making explicit connections between causal emergence and the channel capacity of a model.
• Hoel, E., Albantakis, L., & Tononi, G. (2013). Quantifying causal emergence shows that macro can beat micro. Proceedings of the National Academy of Sciences. 110 (49) 19790-19795. doi: 10.1073/pnas.1314922110.
  • the first work to quantify causal emergence, showing how and why certain coarse-grained models can have more effective information.