Sampling bipartite graphs with given vertex degrees and fixed edges and non-edges
Abstract
We consider the problem of sampling a bipartite graph with given vertex degrees where a set F of edges and non-edges which need to be contained is predefined. Our general result shows that the repeated swap of edges and non-edges in alternating cycles of at most size 2-2 ('j-swaps' with j ≤ 2 -2) in a current graph lead to an ergodic Metropolis Markov chain whenever F does not contain a cycle of length 2 with ≥ 4. This leads to useful Markov chains whenever is not too large. If F is a forest, 4- and 6-swaps are sufficient. Furthermore, we prove that 4-swaps are sufficient when F does not contain a matching of size 3. We extend the Curveball algorithm of Strona et al. Strona2014b to our cases.
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