These graphs represent the network created by tumblr bloggers who reblogged a previous post of mine. The first graph corresponds to the network formed after 2 days, and the second one is the same network after 3 days. In both networks, there are some clusters, where a blogger reblogs my post and after that successive rebloggings are occuring from his/her followers. I created a little program in Mathematica, which can read the notes of the post and identify who reblogged from whom.
I have attributed a name to some of these clusters by the name of the blog located in the root of the cluster. For example, my cluster is the number 1. The biggest cluster though, for the first graph, is that of jtotheizzoe. For the second graph, the huge cluster is that of n-a-s-a, which has its origin from the jtotheizzoe’s cluster (number 2)… The seperated couples at the bottom are users that have reblogged my post by the ‘likes’ list’ of the other user, and then I couldn’t know where they came from…
I really enjoy that, and I’m curious how the structure of the network will look like eventually…
This a very cool analysis of Tumblr post spread. It’s very interesting to see how content spreads over days from the original poster, and how its life span and amplification change. It’s sharing, visualized.
I’m happy to be a node on this, as well.
I have wanted to write something to do this for a while now.
London NCL Social Network Graph - The network is built from nodes and edges, were the nodes are the twitter users active during the time period of message collection back in May 2010. The edges visualise the connections between these users. From the messages sent connections are established based on activity and interaction. In reality these are the @ messages that are directed at one or more particular user. The second indicator of a connection are the RT messages, the message that have been retweeted by followers of the creator of the initial message.
The resulting network is built from a total of 17618 nodes and 26445 edges. In the case of this London twitter network not everyone is connected to everyone and about 5400 subnetworks were identified. Furthermore via the colouring the modularity of the network is visualised. Each subgroups has a unique colour shading indicating groups with tighter connections.