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Parallel Adaptive Algorithms For Sampling Large Scale Networks, Kanimathi Duraisamy
Parallel Adaptive Algorithms For Sampling Large Scale Networks, Kanimathi Duraisamy
Student Work
The study of real-world systems, represented as networks, has important application in many disciplines including social sciences [1], bioinformatics [2] and software engineering [3]. These networks are extremely large, and analyzing them is very expensive. Our research work involves developing parallel graph sampling methods for efficient analysis of gene correlation networks. Our sampling algorithms maintain important structural and informational properties of large unstructured networks. We focus on preserving the relative importance, based on combinatorial metrics, rather than the exact measures. We use a special subgraph technique, based on finding triangles called maximal chordal subgraphs, which maintains the highly connected portions …