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Parallel global edge switching for the uniform sampling of simple graphs with prescribed degrees
(2023)
The uniform sampling of simple graphs matching a prescribed degree sequence is an important tool in network science, e.g. to construct graph generators or null-models. Here, the Edge Switching Markov Chain (ES-MC) is a common choice. Given an arbitrary simple graph with the required degree sequence, ES-MC carries out a large number of small changes, called edge switches, to eventually obtain a uniform sample. In practice, reasonably short runs efficiently yield approximate uniform samples.
In this work, we study the problem of executing edge switches in parallel. We discuss parallelizations of ES-MC, but find that this approach suffers from complex dependencies between edge switches. For this reason, we propose the Global Edge Switching Markov Chain (G-ES-MC), an ES-MC variant with simpler dependencies. We show that G-ES-MC converges to the uniform distribution and design shared-memory parallel algorithms for ES-MC and G-ES-MC. In an empirical evaluation, we provide evidence that G-ES-MC requires not more switches than ES-MC (and often fewer), and demonstrate the efficiency and scalability of our parallel G-ES-MC implementation.
Uniform sampling from the set G(d) of graphs with a given degree-sequence d=(d1,…,dn)∈Nn is a classical problem in the study of random graphs. We consider an analogue for temporal graphs in which the edges are labeled with integer timestamps. The input to this generation problem is a tuple D=(d,T)∈Nn×N>0 and the task is to output a uniform random sample from the set G(D) of temporal graphs with degree-sequence d and timestamps in the interval [1,T]. By allowing repeated edges with distinct timestamps, G(D) can be non-empty even if G(d) is, and as a consequence, existing algorithms are difficult to apply.
We describe an algorithm for this generation problem which runs in expected time O(M) if Δ2+ϵ=O(M) for some constant ϵ>0 and T−Δ=Ω(T) where M=∑idi and Δ=maxidi. Our algorithm applies the switching method of McKay and Wormald [1] to temporal graphs: we first generate a random temporal multigraph and then remove self-loops and duplicated edges with switching operations which rewire the edges in a degree-preserving manner.
Uniform sampling from the set G(d) of graphs with a given degree-sequence d=(d1,…,dn)∈Nn is a classical problem in the study of random graphs. We consider an analogue for temporal graphs in which the edges are labeled with integer timestamps. The input to this generation problem is a tuple D=(d,T)∈Nn×N>0 and the task is to output a uniform random sample from the set G(D) of temporal graphs with degree-sequence d and timestamps in the interval [1,T]. By allowing repeated edges with distinct timestamps, G(D) can be non-empty even if G(d) is, and as a consequence, existing algorithms are difficult to apply.
We describe an algorithm for this generation problem which runs in expected time O(M) if Δ2+ϵ=O(M) for some constant ϵ>0 and T−Δ=Ω(T) where M=∑idi and Δ=maxidi. Our algorithm applies the switching method of McKay and Wormald [1] to temporal graphs: we first generate a random temporal multigraph and then remove self-loops and duplicated edges with switching operations which rewire the edges in a degree-preserving manner.