Social Networking Analysis

We the “tech-savvy” are dwelling in the world of Web 2.0 and are already on the verge of evolution into Web 3.0. The major factor that contributed to Web 2.0 is social networking – the influence of “people” connections on internet. Facebook is inevitably the major player, but not the first one. Sixdegrees.com launched in 1997 was the first to implement this concept. But this triggered off many online forums and chat rooms which became the sensation of the past decade. Any teenager not on facebook is literally considered to be “anti-social” or a “boring” person...!! It looks like a “really cool” stuff where I can find my second grade friend on facebook but there is a lot more than this. There are a lot of companies out there who try to analyze our networks and find some meaning out of it.

“Social network Analysis” is indeed not as fanciful or glamorous as you see. It involves a lot of calculations. It is the measuring of relationships between people or groups. A node in a network can represent a single person, a group, a tweet, hastag or a community. Edges are the connections between the nodes. The edges can be directed or undirected. For example in twitter you can know who is following whom, so it is a directed network. Apart from this there are “zillion” other terms that reveals different truths about the network. Following are a few question which would help you understand the terms.

Who is the hub for the network?

Degree centrality would give the answer for this question. It measures the most number of direct connections made by a particular node. The degree centrality of the network would be the node which has the maximum degree centrality. That node is the most active, that person is connected to every other person on the network.

Who can access all the other nodes more quickly?           

Closeness centrality of a node tells whether it can access all other nodes quickly or not. The closeness centrality of a network has the shortest paths to every other node in the network.  So the information can spread quickly through this node.

Who can act as a mediator in the network?

Betweeness Centrality gives the probability of a node occurring between any two nodes. So “he” is the person whom you can find between any two your friends.

Who is the most influential person in the network?

Eigen vector weighs every connection that a node has and the eigenvector centrality gives the weighted sum of all connections (direct as well as undirected). The person having the highest eigen vector is the most famous person in the network.