Remove unusual characters from models

Author: Daniel Precioso

modules/networks/models.qmd

@@ -68,7 +68,7 @@ def plot_degree_histogram(G, title="Degree distribution"):
     plt.show()
 ```
 
-## Random graphs (Erdős–Rényi / Gilbert)
+## Random graphs (Erdos–Rényi / Gilbert)
 
 In the **Gilbert** model $G(N, p)$, we consider every possible pair of nodes and include the edge independently with probability $p$.
 
@@ -90,7 +90,7 @@ G_random = nx.erdos_renyi_graph(n=80, p=0.06, seed=SEED)
 
 pos = nx.spring_layout(G_random, seed=SEED)
 nx.draw(G_random, pos=pos, node_size=120, node_color="lightgray", edge_color="gray")
-plt.title("Erdős–Rényi random graph")
+plt.title("Erdos–Rényi random graph")
 plt.show()
 
 summarize_graph(G_random, name="Random (G(N,p))")
@@ -121,7 +121,7 @@ Watts–Strogatz networks start from a **ring lattice** (high clustering, long p
 
 Key properties:
 
-- For small $p$ (e.g. $p \in [0.01, 0.2]$), you often get **high clustering** *and* **short path lengths** (“small-world”).
+- For small $p$ (e.g. $p \in [0.01, 0.2]$), you often get **high clustering** *and* **short path lengths** ("small-world").
 - Degree distribution stays relatively **narrow** (most nodes have degree near $k$).
 
 In NetworkX:
@@ -158,7 +158,7 @@ The **Barabási–Albert (BA)** model generates this effect via **preferential a
 
 Key properties (typical):
 
-- Degree distribution is heavy-tailed (often summarized as “scale-free”).
+- Degree distribution is heavy-tailed (often summarized as "scale-free").
 - Small average path length (hubs create shortcuts).
 - Clustering is usually lower than small-world models (but there are variants that increase clustering).
 
@@ -185,9 +185,9 @@ plot_degree_histogram(G_sf, title="Scale-free: degree distribution")
 ```
 
 ::: {.callout-tip collapse="true"}
-## “Scale-free” in NetworkX
+## "Scale-free" in NetworkX
 
-The BA model is the most common introduction to “scale-free” networks.
+The BA model is the most common introduction to "scale-free" networks.
 NetworkX also provides other generators (some return directed multigraphs), for example:
 
 - `nx.scale_free_graph(n, seed=...)`