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Data Science of the Facebook World—Stephen Wolfram Blog

Data Science of the Facebook World—Stephen Wolfram Blog
More than a million people have now used our Wolfram|Alpha Personal Analytics for Facebook. And as part of our latest update, in addition to collecting some anonymized statistics, we launched a Data Donor program that allows people to contribute detailed data to us for research purposes. A few weeks ago we decided to start analyzing all this data. And I have to say that if nothing else it’s been a terrific example of the power of Mathematica and the Wolfram Language for doing data science. (It’ll also be good fodder for the Data Science course I’m starting to create.) We’d always planned to use the data we collect to enhance our Personal Analytics system. I’ve always been interested in people and the trajectories of their lives. So what does the data look like? So a first quantitative question to ask is: How big are these networks usually? But how typical are our users? So, OK. But what friends do they add? And here’s an interactive version, generated from CDF: OK. But, OK.

Related:  Machine Intelligence

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Alpha Personal Analytics Connect with Faceook, sign in for free, and get unique, personalized information anad analysis on your social data-computed by Wolfram|Alpha Clustering of your friends What are the groups of friends that make up your network? next generation video: Introducing Daala part 2 The last demo page mentioned that Daala's use of lapped transforms affects multiple aspects of the codec's structure. Classic intra-frame prediction is one example of a Catch-22 caused by lapping: We can't predict from neighboring pixels that have not yet been fully decoded, and we can't finish decoding these pixels until we lap them with the current block, which of course can't be decoded until after prediction. To unravel this circular dependency, Daala predicts from neighboring frequency coefficients rather than neighboring pixels. This strategy also makes use of a larger amount of prediction information, as it predicts from several full blocks of coefficients, rather than a thin strip only one or two pixels wide. Above: Dr.

Your Daily Life in GIFs (28 GIFs) Here’s a bunch of those little everyday moments put into GIF form… When someone says, “Think fast!” When you’re wearing socks and step in something wet: When your parents don’t like what you’re wearing: When you take pics with your best friend: