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Facebook-class social network analysis with R and Hadoop

In computing, social networks are traditionally represented as graphs: a connection of nodes (people), pairs of which may be connected by edges (friend relationships). Visually, the social networks can then be represented like this: Social network analysis often amounts to calculating the statistics on a graph like this: the number of edges (friends) connected to a particular node (person), and the distribution of the number of edges connected to nodes across the entire graph. When the graph consists of up to 10 billion elements (nodes and edges), such computations can be done on a single server with dedicated graph software like Neo4j. But bigger networks — like Facebook's social network, which is a graph with more than 60 billion elements — require a distributed solution. Marko A. Rodriguez, a graph consultant with Aurelius, shows in a blog post how to use R and Hadoop (integrated with Revolution Analytics' RHadoop packages) to analyze Facebook-scale social networks. He first simulates a social network (shown at the top of this post) using R's igraph package, and then distributed the network in the Hadoop cluster with to.dfs function (from the rhdfs package). He then used the mapreduce function (from the rmr package) to write a simple map-reduce algorithm in R to count the number of edges associated with each node: degree.V <- over Hadoop

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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