In the planted bisection model a random graph G(n,p_+,p_-) with n vertices is created by partitioning the vertices randomly into two classes of equal size (up to plus or minus 1). Any two vertices that belong to the same class are linked by an edge with probability p_+ and any two that belong to different classes with probability (p_-) c * sqrt((d_+)ln(d_+)) for a certain constant c>0.
Finally, there has been recent progress on determining the minimum bisection width on the - random graph. Although its precise asymptotics remain unknown in the case of bounded average degrees @math , it was proved in @cite_13 that the main correction term corresponds to the Parisi formula'' in the Sherrington-Kirkpartrick model @cite_35 . Additionally, regarding the case of very sparse random graphs, there is a sharp threshold for the minimum bisection width to be linear in @math @cite_14 .
@cite_35 @cite_14 @cite_13
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For Erdős–Renyi random graphs with average degree γγ, and uniformly random γγ-regular graph on nn vertices, we prove that with high probability the size of both the Max-Cut and maximum bisection are n(γ4+P∗γ4−−√+o(γ−−√))+o(n)n(γ4+P∗γ4+o(γ))+o(n) while the size of the minimum bisection is n(γ4−P∗γ4−−√+o(γ−−√))+o(n)n(γ4−P∗γ4+o(γ))+o(n). Our derivation relates the free energy of the anti-ferromagnetic Ising model on such graphs to that of the Sherrington–Kirkpatrick model, with P∗≈0.7632P∗≈0.7632 standing for the ground state energy of the latter, expressed analytically via Parisi’s formula. Consider partitions of the vertex set of a graph G into two sets with sizes differing by at most 1: the bisection width of G is the minimum over all such partitions of the number of ‘‘cross edges’’ between the parts. We are interested in sparse random graphs Ž . G with edge probability c n. We show that, if c ln 4, then the bisection width is n n, c n with high probability; while if c ln 4, then it is equal to 0 with high probability. There are corresponding threshold results for partitioning into any fixed number of parts. 2001 John Wiley & Sons, Inc. Random Struct. Alg., 18, 31 38, 2001 ean distance is at most r, for some prescribed r. We show that monotone properties for this class of graphs have sharp thresholds by reducing the problem to bounding the bottleneck matching on two sets of n points distributed uniformly in [0, 1] d . We present upper bounds on the threshold width, and show that our bound is sharp for d = 1 and at most a sublogarithmic factor away for d ≥ 2. Interestingly, the threshold width is much sharper for random geometric graphs than for Bernoulli random graphs. Further, a random geometric graph is shown to be a subgraph, with high probability, of another independently drawn random geometric graph with a slightly larger radius; this property is shown to have no analogue for Bernoulli random graphs. In this paper we explore first passage percolation (FPP) on the Erd˝ os-Renyi random graph Gn(pn), where each edge is given an independent exponential edge weight with rate 1. In the sparse regime, i.e., when npn → λ > 1, we find refined asymptotics both for the minimal weight of the path between uniformly chosen vertices in the giant component, as well as for the hopcount (i.e., the number of edges) on this minimal weight path. More precisely, we prove a central limit theorem for the hopcount, with asymptotic mean and variance both equal to λ (λ − 1) log n. Furthermore, we prove that the minimal weight centered by log n (λ − 1) converges in distribution. We also investigate the dense regime, where npn → ∞. We find that although the base graph is a ultra small (meaning that graph distances between uniformly chosen vertices are o(log n)), attaching random edge weights changes the geometry of the network completely. Indeed, the hopcount Hn satisfies the universality property that whatever be the value of pn, Hn log n → 1 in probability and, more precisely, (Hn−βn log n) √ log n, where βn = λn (λn− 1), has a limiting standard normal distribution. The constant βn can be replaced by 1 precisely when λn ≫ √ log n, a case that has appeared in the literature (under stronger conditions on λn) in (2, 12). We also find bounds for the maximal weight and maximal hopcount between vertices in the graph. This paper continues the investigation of FPP initiated in (2) and (3). Compared to the setting on the configuration model studied in (3), the proofs presented here are much simpler due to a direct relation between FPP on the Erd˝ os-Renyi random graph and thinned continuous-time branching processes.