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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > RANDOM GRAPHS:
Reports tagged with random graphs:
TR00-011 | 27th January 2000
Sotiris Nikoletseas, Paul Spirakis

Efficient Communication Establishment in Extremely Unreliable Large Networks

We consider here a large network of $n$ nodes which supports
only the following unreliable basic communication primitive:
when a node requests communication then this request
{\em may fail}, independently of other requests, with probability
$f<1$. Even if it succeeds, the request is answered by returning
a stable link to ... more >>>


TR00-043 | 21st June 2000
Uriel Feige, Marek Karpinski, Michael Langberg

A Note on Approximating MAX-BISECTION on Regular Graphs


We design a $0.795$ approximation algorithm for the Max-Bisection problem
restricted to regular graphs. In the case of three regular graphs our
results imply an approximation ratio of $0.834$.

more >>>

TR03-045 | 8th June 2003
Oded Goldreich, Asaf Nussboim

On the Implementation of Huge Random Objects

Revisions: 1


We initiate a general study of pseudo-random implementations
of huge random objects, and apply it to a few areas
in which random objects occur naturally.
For example, a random object being considered may be
a random connected graph, a random bounded-degree graph,
or a random error-correcting code with good ... more >>>


TR03-073 | 11th June 2003
Amin Coja-Oghlan

The Lovasz number of random graph

We study the Lovasz number theta along with two further SDP relaxations $\thetI$, $\thetII$
of the independence number and the corresponding relaxations of the
chromatic number on random graphs G(n,p). We prove that \theta is
concentrated about its mean, and that the relaxations of the chromatic
number in the case ... more >>>


TR04-012 | 19th December 2003
Paul Beame, Joseph Culberson, David Mitchell, Cristopher Moore

The Resolution Complexity of Random Graph $k$-Colorability

We consider the resolution proof complexity of propositional formulas which encode random instances of graph $k$-colorability. We obtain a tradeoff between the graph density and the resolution proof complexity.
For random graphs with linearly many edges we obtain linear-exponential lower bounds on the length of resolution refutations. For any $\epsilon>0$, ... more >>>


TR05-004 | 3rd January 2005
Leslie G. Valiant

Memorization and Association on a Realistic Neural Model

A central open question of computational neuroscience is to identify the data structures and algorithms that are used in mammalian cortex to support successive acts of the basic cognitive tasks of memorization and association. This paper addresses the simultaneous challenges of realizing these two distinct tasks with the same data ... more >>>


TR06-116 | 19th July 2006
Amin Coja-Oghlan

Graph partitioning via adaptive spectral techniques

We study the use of spectral techniques for graph partitioning. Let G=(V,E) be a graph whose vertex set has a ``latent'' partition V_1,...,V_k. Moreover, consider a ``density matrix'' E=(E_vw)_{v,w in V} such that for v in V_i and w in V_j the entry E_{vw} is the fraction of all possible ... more >>>


TR12-015 | 22nd February 2012
Albert Atserias, Anuj Dawar

Degree Lower Bounds of Tower-Type for Approximating Formulas with Parity Quantifiers

Revisions: 2

Kolaitis and Kopparty have shown that for any first-order formula with
parity quantifiers over the language of graphs there is a family of
multi-variate polynomials of constant-degree that agree with the
formula on all but a $2^{-\Omega(n)}$-fraction of the graphs with $n$
vertices. The proof yields a bound on the ... more >>>


TR13-055 | 5th April 2013
David Gamarnik, Madhu Sudan

Limits of local algorithms over sparse random graphs

Local algorithms on graphs are algorithms that run in parallel on the nodes of a graph to compute some global structural feature of the graph. Such algorithms use only local information available at nodes to determine local aspects of the global structure, while also potentially using some randomness. Recent research ... more >>>




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