Introduction to the sknet Package
Explore the sknet package and its structure for applying machine learning to complex networks. Learn how to construct networks from tabular data and perform community detection and classification tasks using Python.
We'll cover the following...
The sknet (or scikit-net) package is a Python library that provides machine learning algorithms for complex networks. Let’s learn about how the package is organized so we can use it to apply a different community detection algorithm to our networks. It’s important to notice that community detection can be considered a machine learning task in the same way that a clustering algorithm is.
Sknet general organization
The library is roughly organized into four different sections:
Network construction: The methods available here are responsible for creating networks from tabular datasets and time series and vice-versa. This allows us to apply graph-specific machine learning tasks to different types of data.
Supervised methods: These are the methods such as classification and regression algorithms are present here. They use available labels to predict new ones.
Unsupervised methods: These are the methods that don’t require the availability of labels. Examples include community detection and anomaly detection ...