The Swendsen-Wang process does not always mix rapidly
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
The phase transition in a random hypergraph
Journal of Computational and Applied Mathematics - Special issue: Probabilistic methods in combinatorics and combinatorial optimization
On the Fluctuations of the Giant Component
Combinatorics, Probability and Computing
Large-deviations/thermodynamic approach to percolation on the complete graph
Random Structures & Algorithms
Local Limit Theorems for the Giant Component of Random Hypergraphs
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
A new approach to the giant component problem
Random Structures & Algorithms
Approximating the partition function of the ferromagnetic Potts model
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Approximating the partition function of the ferromagnetic Potts model
Journal of the ACM (JACM)
Asymptotic normality of the size of the giant component in a random hypergraph
Random Structures & Algorithms
Hi-index | 0.00 |
For 0 p q 0 let Gq(n,p) denote the random graph with vertex set [n]={1,…,n} such that, for each graph G on [n] with e(G) edges and c(G) components, the probability that Gq(n,p)=G is proportional to $p^{e(G)}(1-p)^{({n \atop 2})-e(G)}q^{c(G)}$. The first systematic study of Gq(n,p) was undertaken by [Bollobás, Grimmett, and Janson (Probab Theory Relat Fields 104 (1996), 283–317)], who analyzed the phase transition phenomenon corresponding to the emergence of the giant component. In this paper we describe the structure of Gq(n,p) very close the critical threshold. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2006