Facebook-class social network analysis with R and Hadoop
From the post:
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:
[graphic omitted]
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.
Pointer to a Marko A. Rodriguez post that describes how to use R and Hadoop on networks of scale.
Worth your time.