An impossibility result for Markov Chain Monte Carlo sampling from micro-canonical bipartite graph ensembles
Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato

TL;DR
This paper proves that for certain bipartite graph ensembles, no universal small rewiring step can connect all graphs, implying limitations on efficient MCMC sampling methods for these ensembles.
Contribution
It establishes an impossibility result showing that no fixed small number of edge rewirings guarantees full connectivity in some bipartite graph ensembles.
Findings
No universal constant c ensures connectivity with c-edge rewirings.
Constructs pairs of graphs requiring at least c edges to rewire.
Highlights limitations for efficient MCMC sampling in these ensembles.
Abstract
Markov Chain Monte Carlo (MCMC) algorithms are commonly used to sample from graph ensembles. Two graphs are neighbors in the state space if one can be obtained from the other with only a few modifications, e.g., edge rewirings. For many common ensembles, e.g., those preserving the degree sequences of bipartite graphs, rewiring operations involving two edges are sufficient to create a fully-connected state space, and they can be performed efficiently. We show that, for ensembles of bipartite graphs with fixed degree sequences and number of butterflies (k2,2 bi-cliques), there is no universal constant c such that a rewiring of at most c edges at every step is sufficient for any such ensemble to be fully connected. Our proof relies on an explicit construction of a family of pairs of graphs with the same degree sequences and number of butterflies, with each pair indexed by a natural c, and…
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Age of Information Optimization · Radiation Effects in Electronics
