Data Visualization: Network Analysis of Facebook

These days I am so attracted by the Network Graph and spend hours of hours in enjoying the beauty of relationship of nodes and edges.

Advantages

Network Graph is a data visualization technique for revealing the connections between each node and edge. The nodes can be defined as entities like people and edges can be defined as friendship.

The beauty of Network Graph lies in the simplicity of representation of complex relationship using graph. With traditional matrix representation, each matrix has multiple rows as a relationship between each entity.  Each matrix can also have additional columns or rows to present the clusters of similar entities. Compared with the traditional matrix, the Network Graph uses nodes and edges to physically connect each other and also uses different colors to represent different clusters of similar entities.

The other advantage of Graph Network is that it attract people to explore more with crystal clear image and bright color. Researchers will never be drown at flow of black and white tables again.

The following will cover:
1. Tutorial of Network Graph of Facebook
2. Network Analysis of Facebook Connections

1. Tutorial of Network Graph of Facebook
Example: Facebook network Analysis using Graph

Everyone has a facebook account, well, at least one. I assume. Have you ever thought a way to find out the relationship among you and your pals? In fact, Facebook itself has a data visualization app called "Challenger Network Graph". It will create the dumb network graph as below:

Don't use that unless you are too busy to read this blog.

We will use a free software called "Gephi" to create our own network graph and it will be much better than this one from this app, like this:

Here are the tutorial:

1. Go to gephi.org and download the amazing software for free.

2. Go to your facebook page and search for "netvizz" app. It will generate "gdf" file for gephi.

3. Open gdf file with gephi and click OK.

You will see the original data as follows, which is not in order and meaningless:

The next step you will do is to find the clusters that share the common features, eg. friends at the same location.

4. Choose the data layout

    In the layout tab on the left hand side, you have a drop down list to select how you want to let the data visualization be. I prefer Fruchterman Reingold, which is a round shape of data.

5. Run Modularity to identify different clusters.
    The Modularity button is located at the right hand side to the panel under statistics tab. This will let gephi generate a new variable "modularity" for different clusters.

6. Run HITS button to calculate size of each node.
    The size of the nodes is determined by its influence in the network.

7. Select to color different clusters by choose Modularity Class from Partition Tab on the left.

    If you are not happy with the colors, just right click in the area and choose randomized color. You may find the ideal combination eventually.

8. Select to adjust the node size based on HITS calculation.
    From Ranking Tab, select the diamond shape button in the node tab to apply to different node size.

9. Now we need to label each node to see who is really influential to the network.
    Click the "T" button on the bottom

    Then click the "A" button to scale the lable based on influence.

10. We are almost done, check the preview of the network graph by click "Preview" button on the top of screen.

11. Select "Label" in the left side bar to show label in the preview and click "Refresh" button to see the preview. Every time you make a change, you need to refresh the graph.

12. The last thing you need to do is to export the graph to either "PNG", "SVG" or "PDF".
      Sometimes, when you export the file, some labels are not shown in the graph. You can choose the "Options" button to change the width and length of the output.

13. It's done! The full picture will be like this.
      Is it much better than the default Facebook network graph? At least, you did it by yourself! Congrats!

14. Sometimes they graph is too complicated with thousands of nodes and millions of edges, you can use zoom.it to embed your large graph in your web page, like this:

2. Facebook Network Analysis:

Parameters:
Size of Nodes: The size of each node means how valuable that node is. In this example, it indicates how influence it is for the whole network. In other words, how much posts and comments for the network. The large the node, the more influence it has for the network.


Color of Nodes/Edges: The color indicates a cluster of people with similar features. In this example, location is the feature to separate group of friends.


Number of Edges: Each node can have at least one edge to the other nodes for connection. The more edge it has the more people it connects.

From the Facebook Network Graph, it is clear that I have mainly two groups of friends, the red group and the blue group. I know they are separated by the location: red are from Chicago and blue ones are from Tucson, AZ.

There are other colors specifying friends neither in Chicago nor in Tucson. They are covered by the flow of red and blue...

For each one in read and blue, he or she is well connected with each group, because the edges or lines are crossed over each other. The connections are made by both people agree to add each other as a friend. Therefore, there are no directions in the edges. The connections are based on mutual acknowledgement. In the middle of red and blue, it is so crowded that it is not easy to identify the actual nodes.

For blue network, the most influential people are Joseph Yu, Anagela Cheng, and Qiao Meng. For the red network, Dongping Xie is the only one that has great influence. Notice the node labels are also adjusted by the influence.

The two groups even have one connection in common, which means this node relates the two groups of red and blue. Qiao Meng is well connected with the blue group but also has one connection with Fay Peng from the read group. He is the key person connecting the two groups.

Notice some people have only large amount of connections but with only small node size. This means that person does not post comments too often in the network, even he or she is rich in friendship. Some people has small node size and only one connection, which means he or she is not too involved in this network.

To conclude, the network analysis is very simple but it easily reveals some details by simply looking at the graph. If the data is presented from matrix or tables, it will be time consuming and cumbersome to get the similar conclusion as I did in here. Network Graph saves valuable time and boosts efficiency in research process.