Tutorials

Exploring Complex Networks: Theory, Metrics, and Structure Detection in R & Python

by Andrea  Montano Ramirez

In this hands-on tutorial, we will dive into the fascinating world of complex networks—from fundamental concepts to advanced structural patterns—through a practical and comparative lens using R and Python. We will begin with a concise introduction to network science, exploring real-world examples from social systems, biology, and infrastructure. Participants will learn how to construct, visualize, and manipulate networks using popular packages like igraph (in R and Python) and networkx (in Python). The tutorial will cover key network metrics—degree distribution, centrality measures (betweenness, closeness, eigenvector), clustering coefficients, and assortativity—as tools to quantify structure and identify important nodes. Moving beyond basic measures, we will introduce k-core decomposition to uncover hierarchical node organization and core-periphery structure detection to differentiate densely connected hubs from sparse peripheries. Through hands-on coding exercises and real datasets, we will illustrate how these methods provide insight into the robustness, vulnerability, and functional roles of different parts of a network.

Investigating higher-order interactions using a recently developed Python toolbox: HOI

by Matteo Neri

Over the past two decades, network science has provided novel perspectives across a wide range of research questions and complex systems. Traditionally, however, network-based approaches have focused primarily on pairwise interactions, often neglecting interactions involving simultaneously three or more elements—referred to as higher-order interactions (HOIs). A growing body of research has recently underscored the critical role of HOIs in shaping the behavior of complex systems. Taking into account HOI can yield more accurate and comprehensive explanations of diverse phenomena, from information integration in the brain to the spread of epidemics in society.

But how can we effectively study HOIs in complex systems? One promising approach is grounded in information theory, which allows for the quantification of HOIs through measures of synergy, redundancy, and other informational properties. To support this line of research and to make the investigation of HOI feasible in practice, we have developed HOI (https://github.com/brainets/hoi), a flexible and efficient Python toolbox for computing these metrics on any type of multivariate data. Built with cutting-edge tools like JAX (https://docs.jax.dev/en/latest/quickstart.html), the toolbox combines computational efficiency with ease of use. Users can begin analyzing their own data with just a few lines of code, while those with more experience in the field can leverage the modular design to develop new metrics.

An introduction to graph-tool and statistical inference using minimum description length principle

by Erik Weis

Graph-tool is a powerful software package for performing various network analysis tasks, including network community detection and network inference. While this family of algorithms can be used out of the box with minimal coding effort, scientific application of these tools requires a more thorough understanding of how they work. The first goal of this tutorial is to provide a basic intuition for their conceptual core of graph-tool methods—i.e., the minimum description length (MDL) principle. Then, we will survey different algorithmic variants including community detection for weighted graphs, multilayer graphs, bipartite graphs, or sequence data and network reconstruction from noisy data or time series. Finally, we will cover the various approaches to statistical inference, including a brief overview of inference algorithms and their hyperparameters. Attendees should leave with a basic intuition for how to justify their use of MDL-based methods in scientific research, as well as a practical understanding of how to use graph-tool for such purposes.

Manipulating temporal networks using NetworkX-Temporal

by Nelson Aloysio Reis De Almeida Passos

NetworkX-Temporal is a Python library designed for building and manipulating temporal graphs. It implements functions to slice, transform, and convert dynamic graph data to different formats and libraries, allowing researchers and practitioners to employ it for various analysis and exploration tasks. This tutorial aims to provide a practical introduction with hands-on examples, including calculating node centrality and graph centralization, drawing and visualizing graphs, and detecting and tracking communities over time. A basic understanding of coding in Python is assumed, and prior experience with network analysis in Python is beneficial, but not mandatory.