Network inference tutorial

Slides and code for my Network Inference from time-series data tutorial.

(c) Joseph T. Lizier, 2025-.

Slides

See 202506-NetSci-NetworkInferenceTutorial.pdf.

Contents:

• Philosophy: functional, effective and structural connectivity:
• Approaches/measures
• Considerations

Notebooks

There are two main demonstration notebooks here:

1. NetworkInference_CAs.ipynb to run inference on Elementary Cellular Automata data, using non-linear measures (mutual information, transfer entropy, multivariate transfer entropy)
2. NetworkInference_Linear.ipynb to run inference on various data sets (synthetic VAR, stock closing prices, fMRI) with linear measures (correlation, least squares regression)

Requirements:

To run the notebooks, you require Python v3 with packages:

1. The usual suspects: numpy, scipy, sys, matplotlib, os
2. Specific packages for finance (yfinance, pandas) and neuroscience (nilearn) examples
3. jpype1 -- it's important that you install jpype1 rather than jpype!

A Java runtime (JRE) installation, so that python's jpype1 can call it, and to run the AutoAnalyser from JIDT to generate Java code.

References:

• JIDT -- toolkit for information-theoretic measures
• IDTxl -- toolkit for effective network inference with information-theoretic measures
• pyspi -- toolkit for many pairwise statistical measures which could be used for functional connectivity
• assessing-linear-dependence -- toolkit for handling autocorrelations for linear measures

Licences:

This repo is distributed under GPLv3.

It redistributes the jar file from my JIDT project under GPLv3.

Instances:

This tutorial has been run at:

• NetSci 2025