Effectively Visualizing Large Networks Through Sampling

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  • Technical report TR05-08. We study the problem of visualizing large networks and develop techniques for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduction techniques are based on sampling, where only a sample instead of the full network is visualized. We propose a randomized notion of ``focus'' that specifies a part of the network and the degree to which it needs to be magnified. Visualizing a sample allows our method overcome the scalability issues inherent in traditional visualization methods. We report some characteristics that frequently occur in large networks and the conditions under which they are preserved when sampling from a network. This can be useful in selecting a proper sampling scheme that yields a sample with similar characteristics as the original network. Our method is built on top of a relational database, thus it can be easily and efficiently implemented using any off-the-shelf database software. As a proof of concept, we implement our methods within a system called ALVIN and report some of our experiments over the movie database and the connectivity graph of the Web with 178 million nodes and over 800 million edges. | TRID-ID TR05-08

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    Attribution 3.0 International