Stephen Curial

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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(More)
This paper describes Memory-Pooling-Assisted Data Splitting (MPADS), a framework that combines data structure splitting with memory pooling --- Although it MPADS may call to mind memory padding, a distintion of this framework is that is does not insert padding. MPADS relies on pointer analysis to ensure that splitting is safe and applicable to type-unsafe(More)
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