Tutorial¶

This section provides interactive tutorials to help you get started with ASH (Attributed Stream Hypergraph). Each tutorial is a Jupyter notebook that demonstrates different aspects of the library.

All tutorial notebooks are available in the tutorial/ folder of the repository.

Tutorial notebooks (links on GitHub)¶

The notebooks are kept in the repository under the tutorial/ folder and are intended to be opened on GitHub or in your local Jupyter environment. The documentation build no longer renders notebooks into the HTML site; instead use the links below to view or download the notebooks from GitHub:

Getting Started¶

The tutorials are organized in a progressive manner:

Basics (00-basics.ipynb): Learn how to create an ASH object, add nodes and hyperedges, and access basic properties and measures.
Attribute Analysis (01-attribute_analysis.ipynb): Explore purity/entropy measures on hyperedge profiles, homogeneity, and temporal consistency of attributes.
Walks (02-walks.ipynb): Understand s-walks, distances, and s-based components in the temporal context.
Generators (03-generators.ipynb): Generate random hypergraphs and transform external structures into ASH objects.
I/O Operations (04-io.ipynb): Read and write ASH structures in various formats including CSV profiles, JSON, and HIF format.
Multi-Ego Networks (05-multiego.ipynb): Work with multi-ego network extraction and analysis.
Segregation Analysis (06-segregation.ipynb): Measure and analyze segregation patterns in attributed hypergraphs.

Running the Notebooks¶

To run the tutorial notebooks locally:

Clone the repository:

git clone https://github.com/GiulioRossetti/ASH.git
cd ASH

Install the required dependencies:
```
pip install -r requirements.txt
```
Launch Jupyter:
```
jupyter notebook tutorial/
```
Or open the notebooks directly in VS Code with the Jupyter extension.