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Technical Perspective: Graphs, Betweenness Centrality, and the GP­U

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Graphs are the natural data structures to represent relationships, and in our age of big data, graphs are very big indeed. For instance, Facebook's social graph has well over two billion users (vertices in the graph), and their friendships (edges in the graph) may number in the hundreds of billions. How do we make sense of data this large?

If possible, we can gain significant insight into complex problems of interest both to commerce and to science. Through graph data, we may be able to detect anomalies (say, intrusions into a computer network), make recommendations (say, which movie to watch), search a graph for patterns (say, credit card fraud), or detect communities (say, identifying proteins within a cell with similar functionality). Enabling faster graph computation allows us to find answers to these questions more quickly and cheaply.


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