More Efficient Social Graph and Semantic Analysis

June 30, 2010

Short honk: My hunch is that the University of Maryland has come up with a nifty method to deal with some cumbersome and computationally intensive computations. Navigate to “Scientists Develop World’s Fastest Program to Find Patterns in Social Networks” and read about fancy math and chopping big data into chunks. With the technique, figuring out patterns gets easier. I will resist a pun about cozying up to big data. Here’s the passage that caught my attention in the write up:

In a paper that has been accepted for presentation at the 2010 Advances in Social Network Analysis and Mining conference to be held in Denmark in August, Broecheler, Pugliese and Subrahmanian [University of Maryland wizards] leveraged a key insight – it is possible to split the social network into a set of almost independent, relatively small sub-networks, each of which is stored on a computer in a cloud computing cluster in such a way that the probability that a query pattern will need to access two nodes is kept as small as possible. Using knowledge of past queries and a complex set of calculations to compute these probabilities, their paper reports algorithms and experiments to answer social network subgraph pattern matching queries on real-world social network data with 778 million edges (which may denote relationships or connections between individuals) in less than one second. More recent results not contained in the paper are able to efficiently answer queries to social network databases containing over a billion edges.

Strikes me as important, particularly for outfits gunning their PT boats toward Fort Google.

Stephen E Arnold, June 30, 2010



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