Categories

# Calculating Degree Centrality Using Data Frames – Python

Calculating Freeman’s degree centrality for each node… …and for the graph. All the information is contained in an external file `graph.csv`, which contains this

```# graph.csv
"nodes","freq"
"n1",3
"n2",1
"n3",1
"n4",2
"n5",1
```

Where the “freq” column is the number of edges adjacent to the node.

To find node centrality

```#!/usr/bin/python
# Node Degree Centrality

import pandas,numpy

# import file
print ":: Node Degree Centrality ::"
fileName=raw_input("Enter file name:\n")

# Calculations
gLength=len(df)-1.0
df["CD"]=(df["freq"]/gLength)

# Output
outputFile="data_"+fileName
df.to_csv(outputFile,index=False)
```

This script outputs the file `data_graph.csv` with the following information:

```# data_graph.csv
nodes,freq,CD
n1,3,0.75
n2,1,0.25
n3,1,0.25
n4,2,0.5
n5,1,0.25
```

Where `CD` is the node’s degree centrality.

The graph’s degree centrality is measured with this

```#!/usr/bin/python
# Graph Degree Centrality

import pandas,numpy

# import file
print ":: Graph Degree Centrality ::"
fileName=raw_input("Enter file name:\n")

# calculations
maxVal=df["freq"].max()
df["f1"]=maxVal-df["freq"]
```Graph CD = 0.583333333333