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We consider the problem of finding a large clique in an Erd\H{o}s--R\'enyi random graph where we are allowed unbounded computational time but can only query a limited number of edges. Recall that the largest clique in $G \sim G(n,1/2)$ has…

Combinatorics · Mathematics 2024-07-12 Endre Csóka , András Pongrácz

We introduce a new conjecture on the computational hardness of detecting random lifts of graphs: we claim that there is no polynomial-time algorithm that can distinguish between a large random $d$-regular graph and a large random lift of a…

Computational Complexity · Computer Science 2024-04-29 Dmitriy Kunisky , Xifan Yu

Extremal graphical models encode the conditional independence structure of multivariate extremes. Key statistics for learning extremal graphical structures are empirical extremal variograms, for which we prove non-asymptotic concentration…

Statistics Theory · Mathematics 2025-11-05 Sebastian Engelke , Michaël Lalancette , Stanislav Volgushev

The maximum likelihood threshold (MLT) of a graph $G$ is the minimum number of samples to almost surely guarantee existence of the maximum likelihood estimate in the corresponding Gaussian graphical model. Recently a new characterization of…

In this paper, we compute the tightest possible bounds on the probability that the optimal value of a combinatorial optimization problem in maximization form with a random objective exceeds a given number, assuming only knowledge of the…

Optimization and Control · Mathematics 2022-11-24 Divya Padmanabhan , Selin Damla Ahipasaoglu , Arjun Ramachandra , Karthik Natarajan

We present deterministic techniques for computing upper and lower bounds on marginal probabilities in sigmoid and noisy-OR networks. These techniques become useful when the size of the network (or clique size) precludes exact computations.…

Artificial Intelligence · Computer Science 2013-02-18 Tommi S. Jaakkola , Michael I. Jordan

We describe the component sizes in critical independent p-bond percolation on a random d-regular graph on n vertices, where d \geq 3 is fixed and n grows. We prove mean-field behavior around the critical probability p_c=1/(d-1). In…

Probability · Mathematics 2007-07-24 Asaf Nachmias , Yuval Peres

A significant generalization of the Erd\"os-R\'enyi random graph model is an `inhomogeneous' random graph where the edge probabilities vary according to vertex types. We identify the threshold value for this random graph with a finite…

Probability · Mathematics 2024-11-06 Hamin Jung

Random planar graphs have been the subject of much recent work. Many basic properties of the standard uniform random planar graph P_{n}, by which we mean a graph chosen uniformly at random from the set of all planar graphs with vertex set…

Combinatorics · Mathematics 2013-07-23 Chris Dowden

We provide simple proofs describing the behavior of the largest component of the Erdos-Renyi random graph G(n,p) outside of the scaling window, p={1+\eps(n) \over n} where \eps(n) tends to 0, but \eps(n)n^{1/3} tends to \infty.

Probability · Mathematics 2007-05-23 Asaf Nachmias , Yuval Peres

In this paper, we study the order of the largest connected component of a random graph having two sources of randomness: first, the graph is chosen randomly from all graphs with a given degree sequence, and then bond percolation is applied.…

Probability · Mathematics 2024-02-20 Lyuben Lichev , Dieter Mitsche , Guillem Perarnau

There are many methods to find a maximum (or maximal) clique in large networks. Due to the nature of combinatorics, computation becomes exponentially expensive as the number of vertices in a graph increases. Thus, there is a need for…

Social and Information Networks · Computer Science 2022-07-27 S. Y. Chan , K. Morgan , J. Ugon

We determine the asymptotic size of the largest component in the $2$-type binomial random graph $G(\mathbf{n},P)$ near criticality using a refined branching process approach. In $G(\mathbf{n},P)$ every vertex has one of two types, the…

Probability · Mathematics 2015-08-14 Mihyun Kang , Christoph Koch , Angélica Pachón

We establish new Bonferroni-type lower bounds for the probability of a union of finitely many events where the selection of intersections in the estimates is determined by the clique complex of a chordal graph.

Combinatorics · Mathematics 2025-12-03 Klaus Dohmen

Consider the random graph on $n$ vertices $1, ..., n$. Each vertex $i$ is assigned a type $X_i$ with $X_1, ..., X_n$ being independent identically distributed as a nonnegative discrete random variable $X$. We assume that ${\bf E}…

Probability · Mathematics 2009-08-18 Tatyana S. Turova

Random walk on changing graphs is considered. For sequences of finite graphs increasing monotonically towards a limiting infinite graph, we establish transition probability upper bounds. It yields sufficient transience criteria for simple…

Probability · Mathematics 2018-10-09 Ruojun Huang

Modeling how networks change under structural perturbations can yield foundational insights into network robustness, which is critical in many real-world applications. The largest connected component is a popular measure of network…

Physics and Society · Physics 2025-09-30 Jessica Jiang , Allison C. Zhuang , Petter Holme , Peter J. Mucha , Alice C. Schwarze

In this paper, we study the task of detecting the edge dependency between two weighted random graphs. We formulate this task as a simple hypothesis testing problem, where under the null hypothesis, the two observed graphs are statistically…

Machine Learning · Computer Science 2024-09-25 Mor Oren , Vered Paslev , Wasim Huleihel

We report further findings on the size distribution of the largest neutral segments in a sequence of N randomly charged monomers [D. Ertas and Y. Kantor, Phys. Rev. E53, 846 (1996); cond-mat/9507005]. Upon mapping to one--dimensional random…

Condensed Matter · Physics 2009-10-28 Deniz Ertas , Yacov Kantor

In this article we present an algorithm to compute bounds on the marginals of a graphical model. For several small clusters of nodes upper and lower bounds on the marginal values are computed independently of the rest of the network. The…

Artificial Intelligence · Computer Science 2011-06-27 B. Kappen , M. Leisink