Remove redundant headings and introductory notes from NetworkX fundamentals and models notebooks

Author: Daniel Precioso

modules/networks/fundamentals.qmd

@@ -3,8 +3,6 @@ title: "NetworkX Fundamentals"
 format: html
 ---
 
-# Network X - Fundamentals
-
 NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
 
 This notebook serves as a brief introduction to the NetworkX package. It covers the following topics:

modules/networks/models.qmd

@@ -3,16 +3,6 @@ title: "NetworkX - Graph Models"
 format: html
 ---
 
-# NetworkX - Graph Models
-
-_The contents of this notebook have been derived from "A First Course in Network Science" by Filippo Menczer. [You can see their official Github repository here.](https://github.com/CambridgeUniversityPress/FirstCourseNetworkScience/tree/master)_
-
-Contents:
-
-1. [Random network model](#1.-Random-Network-Model)
-2. [Small-World model](#2.-Small-World-Model)
-3. [Preferential attachment model](#3.-Preferential-Attachment-Model)
-
 For each of the models presented in this tutorial, we present the algorithm, and the corresponding NetworkX code function to generate the model.
 
 **Note:** This notebook assumes you know the basics of Network X, that were shown in the "fundamentals" notebook. It will also use concepts from the "structural properties" notebook.
@@ -22,7 +12,7 @@ import matplotlib.pyplot as plt
 import networkx as nx
 ```
 
-## 1. Random Network Model
+## Random Network Model
 
 The random network model, as formulated by Gilbert, has two parameters: the number of nodes $N$, and the link probability $p$. As in the book text, the algorithm for creating this network is as follows:
 
@@ -70,7 +60,7 @@ plt.ylabel("Number of nodes")
 plt.show()
 ```
 
-### Erdös-Rényi random graph model
+### Erdos-Renyi random graph model
 
 Instead of specifying the link probability $p$, we can also generate a graph of $N$ nodes with exactly $M$ edges by using `random.sample()` to choose M of the possible edges generated with `itertools.combinations()`:
 
@@ -115,7 +105,7 @@ plt.show()
 
 ---
 
-## 2. Small-World Model
+## Small-World Model
 
 The algorithm for generating a small-world network is as such:
 
@@ -161,7 +151,7 @@ plt.show()
 
 ---
 
-## 3. Preferential Attachment Model
+## Preferential Attachment Model
 
 The Barabási-Albert preferential attachment model has two parameters: the number of nodes $N$, and the number of links added at each step $m$. Given these parameters, the algorithm is as follows:
 
@@ -195,6 +185,7 @@ plt.show()
 ```
 
 ---
+
 ## What's Next?
 
 Ready to simulate epidemics? Continue to: