Part of project: UIUC Affiliation Network

In the previous post, we scraped the data from the web. If you look in the source code, I’ve included a little snippet on how to get the data in a nested dictionary to make it easy to dump to json. Here, I will discuss a much easier alternative using the NetworkX library.

2. Creating the network

The output file uiucData.txt is essentially an edgelist consisting of two categories of nodes, department and people. We will categorize each nodes to one or the other.

import networkx as nx
from networkx.readwrite import json_graph
import json
import pandas as pd

df = pd.read_csv("uiucData.txt", header = None, delimiter = '\t')
df.columns = ['Department', 'People']
department = df.Department.unique()
people = df.People.unique()

Using pandas, we read the data file which is tab-delimited. We know that the left column corresponds to the department and the right correspond to people so it is very simple to obtain a unique list of each. Now, we can create the network from the dataframe.

G = nx.from_pandas_dataframe(df, 'Department', 'People')
d =

Pretty simple, huh? We use degree() to obtain the degree of each node because we eventually want to scale the size of the nodes based on how connected it is. Now, we need to output this network to a json network format to be able to create the visualization. The structure we want looks like so:

"nodes": [
	"name": "Orzek, Marvis Ann", 
	"group": "People"
	"id": "Orzek, Marvis Ann"
"links": [
	"source": 0,
	"target": 110

Thankfully, this is very easy to do with NetworkX with some minor tweaks to the network.

for n in G:
    G.node[n]['name'] = n
    G.node[n]['value'] = (((d[n] - min(d.values())) * (10 - 1)) / (max(d.values()) - min(d.values()))) + 1
    if n in department:
        G.node[n]['group'] = 'Department'
        G.node[n]['group'] = 'People'

Iterating through the network, we gave the network a name based on its id. We then assign a value to the nodes based on its degree. We use max-min scaling because there are some nodes with very high degrees and most have only 1. We also assign the group to the node based on the list obtained from the dataframe.

Now you just need to dump the network as a json and viola, you’re done!

d = json_graph.node_link_data(G)
json.dump(d, open('uiuc.json','w'))

In the next post, we will visualize the network with d3.js.