Hello World! – John McLevey

title: Nested Stochastic Blockmodels Part 1 author: John McLevey date: 2026-02-03 excerpt: An introduction to nested stochastic blockmodels using graph-tool. execute: echo: true warning: false message: false format: gfm: default —

This is the first in a series of posts exploring nested stochastic blockmodels (NSBMs) for network analysis. We’ll use the graph-tool library to fit and visualize these models.

Introduction

Stochastic blockmodels are generative models for networks that assume nodes can be grouped into blocks (communities) with characteristic connection patterns between them.

Setup

Let’s start by importing the necessary libraries.

import pandas as pd
import numpy as np

print("Libraries loaded successfully!")
print(f"NumPy version: {np.__version__}")
print(f"Pandas version: {pd.__version__}")

Libraries loaded successfully!
NumPy version: 2.4.1
Pandas version: 2.3.3

Next Steps

In the next post, we’ll load a real network and fit our first nested SBM. –>