How to create a social network like IBM's Watson News Explorer? I was much impressed by the news-explorer view of IBM.It shows a network view with on searching a keyword and it explores the relational edges with a graph.

Here it is



I think it uses the k-means clustering in background.I too want to do the same for twitter data.I have tweets and users and mentions i want show same a network relation between them.can any one suggest me any graphical data tool which shows the network just like IBM by taking data. 

using clustering algorithms like k-means or any other make my work done?
or do i need to go for any other approach ?
They've created a graph from the news articles, topics, and named entities (locations, persons, companies, organizations). There are a lot of things going on here, but k-means is not one of them. If I had to do this I would use a named entity recognition (NER) and document embeddings (doc2vec, etc.). Once you have the embeddings, and the edges (thanks to NER), use a graph layout algorithm like force direction. If the graph is too dense, thin out the weaker edges. If all that sounds Chinese to you, start by reading about "named entity recognition", and "word embeddings". The idea is to attach a number (or rather, a vector) to everything from a word to a document.

Twitter is a different beast. The textual content, such as it is, won't play nice with these embedding algorithms, but you have hashtags and strong social signals; mentions, retweets, and follows. This is pretty complicated, too, so I'll leave you with a relevant paper: Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling.