Working with Graphs in SciPy

Efficient Representations and Algorithms Using Sparse Matrices

Photo by Ben Mullins on Unsplash

Graphs are essential structures in various computational fields, including computer science, data analysis, and network theory. A graph is a collection of nodes (or vertices) connected by edges. These edges can have weights or directions, depending on the type of graph, and are useful for representing relationships, networks, and even optimization problems.

SciPy provides tools to work with graphs through its scipy.sparse module. Sparse matrices are often used to represent graphs, especially when most possible edges between nodes are absent (which is common in real-world networks). You can find a manual for sparse matrices HERE. A graph can be represented by an adjacency matrix, where each non-zero value represents an edge between two nodes.

Graphs in SciPy

I’ll provide some examples to create a graph in SciPy and solve some problems.

Example 1: Create a graph in SciPy

Lets consider we want to define the 4-nodes graph depicted below in SciPy.