Skip to main content

Facebook Page User Stats & Engagement

Started to play around with the R package "RFacebook"    http://cran.r-project.org/web/packages/Rfacebook/Rfacebook.pdf   to connect to Facebook's API's and access social graph data.
I am posting some of the social graph visualizations that I created using Gephi and ggplot2  ( visualization package in R)

This is a heterogeneous social graph of Facebook page  for my favourite RSS news reader Feedly

Hetrogeneous Graph : The nodes (spheres/dots) on the graph to the left  are
1. Facebook users
2. Facebook posts

The edges on this graph depict connections between nodes, and for this particular graph, I looked at the "likes"
 
The nodes have  also been color coded where blue represents male, pink represents female, and the green is undisclosed. The green nodes are the Facebook page posts by Feedly Facebook admin.
The nodes sizes are also scaled proportionally by the in-degree (# of people who liked a post) , so the larger green nodes were the posts the generated a lot of likes.




I then enabled, the text labels for the nodes to see what those Facebook posts were.

I also set a filter to exclude any nodes with an in-degree < 2 to clear up the space and make the labels readable.













Here is another sample from the Facebook page of Aquimo, an awesome golf simulator that lets you golf using your iPhone.

The game went live in the US iOS store a couple of months and I started to see quiet  a bit of activity on their Facebook page.

Here is a sample visualization of the heterogeneous social graph.











Comments

  1. looks great. what are u using for the visuals? maybe u can call ur self a data artist. simulate picture like patterns and give them a name ( and suggest what is should look like for a favorable output).

    ReplyDelete
  2. Thanks...Used Gephi for the network viz

    ReplyDelete

Post a Comment

Popular posts from this blog

Deep Dreams with Keras & Tensorflow

Made some modifications to the DeepDreamcode courtesy François Chollet and added few extra layers, changed the loss function settings a bit and viola !!!

Used the pre-trained model based on the VGG16 network architecture.

I will post the github link shortly. 



Iteration 1


                                                                            Iteration 2


                                                                          Iteration 3


Iteration 4

Iteration 5


Random !