Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
Simple Markov-chain algorithms for generating bipartite graphs and tournaments
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Systematic topology analysis and generation using degree correlations
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Sampling Regular Graphs and a Peer-to-Peer Network
Combinatorics, Probability and Computing
Statistical analysis of a p2p query graph based on degrees and their time-evolution
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
Efficient and simple generation of random simple connected graphs with prescribed degree sequence
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
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The generation of random graphs using edge swaps provides a reliable method to draw uniformly random samples of sets of graphs respecting some simple constraints (e.g., degree distributions). However, in general, it is not necessarily possible to access all graphs obeying some given constraints through a classical switching procedure calling on pairs of edges. Therefore, we propose to get around this issue by generalizing this classical approach through the use of higher-order edge switches. This method, which we denote by “k-edge switching,” makes it possible to progressively improve the covered portion of a set of constrained graphs, thereby providing an increasing, asymptotically certain confidence on the statistical representativeness of the obtained sample.