October 6, 2012 1 Comment
We are in the era of big data, with newer sources of data emerging at an exponential rate involving sensor data, EHR, social network/media data & machine generated data. In this blog post, I will be discussing specifically about social network data, its applications in data science problems, solutions & visualizations. In simple terms, a network is a group of nodes interconnected by links (also called edges). In a social network, users are the nodes and connections are the links/edges. Consider a Facebook user’s network, by adding friends, we are creating the links. Before getting into a little more of technical details of a network, let’s spend some time on more interesting area – its applications to data science problems.
Linkedin, Facebook & other social network uses the network information, to predict “People you may know” & offer people recommendations. Product companies like Microsoft, Oracle uses network analytics to identify key influencers in leading tech forum/online community networks to help market their products by utilizing the greater reach of the identified influencers. WWW is another example of networks. The pages are interconnected in the form of network & its analysis helps understand information flow across the WWW. “People you may know” feature generally works using triangulation. i.e If B and C are connected. If A knows B, then it is likely that A knows C. Most of the people recommendation work based on this principle.
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