A unifying framework for fast randomization of ecological networks with fixed (node) degrees

Abstract

The switching model is a Markov chain approach to sample graphs with fixed degree sequence uniformly at random. The recently invented Curveball algorithm for bipartite graphs applies several switches simultaneously (`trades'). Here, we introduce Curveball algorithms for simple (un)directed graphs which use single or simultaneous trades. We show experimentally that these algorithms converge magnitudes faster than the corresponding switching models.

References in corpus (4)

• Specificity and stability in topology of protein networks
• Stability Criteria for Complex Ecosystems
• The Babe Ruth Algorithm: a fast, unbiased procedure to randomize presence-absence data matrices with fixed row and column totals
• Factors determining nestedness in complex networks

Cited by in corpus (2)

• Pattern detection in bipartite networks: a review of terminology, applications and methods
• fastball: A fast algorithm to sample bipartite graphs with fixed degree sequences