Enron Email Network

Communication Graph

The Enron email dataset can be represented as a graph where nodes are email addresses and edges represent communication. It is widely used to study centrality, communities, and robustness.

Figure 1: Enron network: degree histogram (log scale).

Load the graph

import networkx as nx

G = nx.read_edgelist("data/ia-enron-only/ia-enron-only.edges")

Centrality metrics

Compute the top nodes by betweenness centrality.

import networkx as nx

bc = nx.betweenness_centrality(G)
# TODO: sort and print top 10 nodes

Hint: Top 10 by betweenness (click to expand)

bc = nx.betweenness_centrality(G)
top10 = sorted(bc.items(), key=lambda x: x[1], reverse=True)[:10]
print(top10)

Questions to explore

Which nodes act as brokers in the network?
How does the graph change if you remove high-betweenness nodes?
Can you detect communities with a simple algorithm?

---
title: "Enron Email Network"
subtitle: "Communication Graph"
format: html
---

The Enron email dataset can be represented as a graph where nodes are email addresses and edges represent communication. It is widely used to study centrality, communities, and robustness.

```{python}
#| label: fig-enron-degree
#| fig-cap: 'Enron network: degree histogram (log scale).'
#| fig-width: 7
#| fig-height: 4
#| echo: false

import matplotlib.pyplot as plt
import networkx as nx

path = "data/ia-enron-only/ia-enron-only.edges"
G = nx.read_edgelist(path)

degrees = [d for _, d in G.degree()]
fig, ax = plt.subplots(figsize=(7, 4))
ax.hist(degrees, bins=50)
ax.set_xlabel("Degree")
ax.set_ylabel("Count")
ax.set_yscale("log")
ax.set_title("Enron degree distribution")
plt.show()
plt.close()
```

## Load the graph

```python
import networkx as nx

G = nx.read_edgelist("data/ia-enron-only/ia-enron-only.edges")
```

## Centrality metrics

Compute the top nodes by betweenness centrality.

```python
import networkx as nx

bc = nx.betweenness_centrality(G)
# TODO: sort and print top 10 nodes
```

::: {.callout-tip collapse="true"}
## Hint: Top 10 by betweenness (click to expand)

```python
bc = nx.betweenness_centrality(G)
top10 = sorted(bc.items(), key=lambda x: x[1], reverse=True)[:10]
print(top10)
```
:::

## Questions to explore

- Which nodes act as brokers in the network?
- How does the graph change if you remove high-betweenness nodes?
- Can you detect communities with a simple algorithm?