Abstract
The graphs that arise from concrete applications seem to correspond to models with prescribed degree sequences. We present two algorithms for the uniform random generation of graphic sequences. We prove their correctness. We empirically evaluate their performance. To our knowledge these algorithms are the first non trivial algorithms proposed for this task. The algorithms that we propose are Markov chain Monte Carlo algorithms. Our contribution is the original design of the Markov chain and the empirical evaluation of mixing time.
This research is supported by NUS grant R-252-000-328-112.
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Lu, X., Bressan, S. (2011). Generating Random Graphic Sequences. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_41
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