Analyzing 2-mode Network using Pajek Part 2

Before in Part 1, I give some introduction about Pajek and how to change 2-mode network into 1-mode network before do the simplified network. So, in this Part, I will show you how to do the centrality.

Centrality is a key concept in social network analysis. It is commonly used to measure the importance or power of a node. The concept is  based on the assumption that the position of a node affects its influence to the other nodes in the networks.

To make it simple, if you have a friend that have more friends than you and her/his position in your network is influential, it might be possible that if he/she do something, other people will people. It's just the example.

There are several centralities, which are:

1. Degree Centrality
which means the one who gather more friends, he/she is more influence.
Example :
There is a prom night and I need help from my friends. Only true friends that will come to help

2. Closeness Centrality
which means the one who has in average closer relationship with all other person is more important.
Example :
If there is a vote for school organization chairman, favoring votes come also from indirect friends.

3. Betweenness Centrality
which means the one who is more able to invite more (through multiple channels).
Example :
If there is a prom, the one who able to invites more hot girls  is more important.

4. Bonacich Centrality and Alpha centrality
which means who your friends are is actually quite important
Example :
If there is a big fight in school between two people. But the one who can escape is the one who has higher relationship with teacher.

In here I only want to show how to measure degree, weighted degree, closeness, and betweenness centrality in network using Pajek.

Open your simplified network like I told you before in the Part 1. 

But if you think it's complicated, you can simplified it again. My network is become like this

Then, do the Centrality with perform this action :
Network - Create Vector - Centrality - Degree - All (degree centrality)
Network - Create Vector - Centrality - Weighted Degree All (weighted degree centrality)
Network - Create Vector - Centrality - Closeness - All (closeness centrality)
Network - Create Vector - Centrality - Betweenness (betwenness centrality)

To draw the network you have to click on Draw - Network + First Vector
You can see in here the results between one centrality and another are different based on what centrality method we want to know.

 Degree Centrality

Weighted Degree Centrality 

Closeness Centrality

Betweenness Centrality

But after see those drawing, how can we know the centrality or the most influence people in the network? You have to export the results using this command Tools - Export to Tab Delimited File - Current Vector - Save File

The saved files are .txt file that you can open using Notepad.

To make it easier, you can move the results to Excel and sort the results for each centrality from highest degree to lowest degree. You can see in the picture below that the results shown people who has high influence in the network are the ones who has yellow color (16 people). But why the result of betweenness centrality is different?

Actually you can connect this result into what each centrality means. Let's break down for each centrality.

1. Degree Centrality and Weighted Degree Centrality
If there's a prom night in school, true friends will come to help. So, in this case 16 people in the yellow color are the people who most likely help their friends in need.

2. Closeness Centrality
If there is a vote for school organization chairman, favoring votes come also from indirect friends. So, in this case if you are one of the candidate, you can ask these 16 people to ask their friends to vote you.

3. Betweenness Centrality
If there is a prom, the one who able to invites more hot girls is more important. But, in this case the degree of all people are 0. I guess no one can invite hot girls/ boys hahaha

But because the case is about students and their dreams in the future, you can connect the result into something in the future. Like for example, they can be marriage partner. For example because Justin Bieber and Norma are most likely have the same preference in their future dreams, they can be marriage partner because they can influence each other.

Hope it can help you and I'm going to continue into Part 3. :)