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Search Publications by: Brian D. Cloteaux (Fed)

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Displaying 1 - 24 of 24

Simulating Job Replication Versus its Energy Usage

December 10, 2023
Author(s)
Brian D. Cloteaux, Vladimir Marbukh
Due to the proliferation of computers in all aspects of our lives, the energy and ecological impacts of computing are becoming increasing important. Some of the transformative algorithms of recent years generate huge amounts of carbon dioxide, potentially

Impact of Using Soft Exposure Thresholds in Automatic Contact Tracing

December 21, 2022
Author(s)
Kamran Sayrafian, Brian D. Cloteaux, Vladimir Marbukh
Current automatic exposure notification apps primarily operate based on hard distance/time threshold guidelines (e.g., 2 m/15 min in the United States) to determine exposures due to close contacts. However, the possibility of virus transmission through

Forced Edges and Graph Structure

November 19, 2019
Author(s)
Brian D. Cloteaux
For a degree sequence, its set of forced edges are the edges that appear in every realization of that sequence, while its forbidden edges appear in no realization. We examine sequences with forced or forbidden edges, showing relationships between these

SIS Contagion Avoidance on a Network Growing by Preferential Attachment

June 30, 2019
Author(s)
Brian D. Cloteaux, Vladimir V. Marbukh
The economic and convenience benefits of interconnectivity drive the current explosive growth in networked systems. However, as recent catastrophic contagious failures in numerous large-scale networked infrastructures have demonstrated, interconnectivity

One-Pass Graphic Approximation of Integer Sequences

December 18, 2017
Author(s)
Brian D. Cloteaux
A variety of network modeling problems begin by generating a degree sequence drawn from a given probability distribution. If the randomly generated sequence is not graphic, we give a new approach for generating a graphic approximation of the sequence. This

Fast Sequential Creation of Random Realizations of Degree Sequences

March 24, 2016
Author(s)
Brian D. Cloteaux
We examine the problem of creating a random realizations of very large degree sequences. While fast in practice, the Markov chain Monte Carlo (MCMC) method for selecting a realization has limited usefulness for creating large graphs because of memory

Is This for Real?: Fast Graphicality Testing

November 1, 2015
Author(s)
Brian D. Cloteaux
When we have an integer sequence that can be realized as the degree sequence of some graph, we say that the sequence is graphic. While almost any graph theory book describes how a sequence can be tested to see if it’s graphic, surprisingly (because this

Limits in Modeling Power Grid Topology

April 29, 2013
Author(s)
Brian D. Cloteaux
Because of their importance to infrastructure, a number of studies have examined the structural properties of power grids and have proposed random topological models of them. We examine the ability to create generalized models of power grid structure by

Threshold Digraphs

December 5, 2012
Author(s)
Brian D. Cloteaux, Michael D. LaMar, Elizabeth R. Moseman, James Shook
A digraph whose degree sequences have a unique vertex labeled realization is called threshold. In this paper we present several characterizations of threshold digraphs and show them to be equivalent. One of the characterizations is new, and allows for a

Counting the Leaves of Trees

December 19, 2011
Author(s)
Brian D. Cloteaux, Luis A. Valentin
A number of important combinatorial counting problems can be reformulated into the problem of counting the number of leaf nodes on a tree. Since the basic leaf-counting problem is #P-complete, there is strong evidence that no polynomial time algorithm

Extracting Hierarchies With Overlapping Structure From Network Data

December 11, 2011
Author(s)
Brian D. Cloteaux
Relationships between entities in many complex systems, such as the Internet and social networks, have a natural hierarchical organization. Understanding these inherent hierarchies is essential for creating models of these systems. Thus, there is a recent

Approximating the Number of Bases for Almost All Matroids

February 1, 2011
Author(s)
Brian D. Cloteaux
We define a class of matroids A for which a fully polynomial randomized approximation scheme (fpras) exists for counting the number of bases of the matroids. We then show that as the number of elements in a matroid increases, the probability that a matroid

Modeling Affiliations in Networks

December 6, 2010
Author(s)
Brian D. Cloteaux
One way to help understand the structure of certain networks is to examine what common group memberships the actors in the network share. Linking actors to their common affiliations gives an alternative type of network commonly called an affiliation

Matching Observed Alpha Helix Lengths to Predicted Secondary Structure

October 11, 2010
Author(s)
Brian D. Cloteaux
Because of the complexity in determining the 3D structure of a protein, the use of partial information determined from experimental techniques can greatly reduce the overall computational expense. We investigate the problem of matching experimentally

An Approximation Algorithm for the Coefficients of the Reliability Polynomial

March 15, 2010
Author(s)
Brian D. Cloteaux, Isabel M. Beichl, F Sullivan
The reliability polynomial gives the probability that a graph remains connected given that each edge in it can fail independently with a probability p. While in general determining the coefficients of this polynomial is #P-complete, we give a randomized

A Structural Approach to the Temporal Modeling of Networks

December 14, 2009
Author(s)
Brian D. Cloteaux, Isabel M. Beichl
Simulation of many dynamic real world systems such as the Internet and social networks requires developing dynamic models for the underlying networks in these systems. Currently, there is a large body of work devoted towards determining the underlying

Matching Observed Alpha Helix Lengths to Predicted Secondary Structure

November 1, 2009
Author(s)
Brian D. Cloteaux, Nadezhda Serova
Because of the complexity in determining the 3D structure of a protein, the use of partial information determined from experimental techniques can greatly reduce the overall computational expense. We investigate the problem of matching experimentally

Lower Bounds for Accessing Information on Pure Pointer Machines

July 13, 2009
Author(s)
Brian D. Cloteaux, Desh Ranjan
We study the complexity of representing an array on a Pure Pointer Machine (PPM) or a pointer machine without arithmetic capabilities. In particular, we show that lower bounds in access time for information retrieval on a PPM arise from two different

Measuring the Effectiveness of the s-Metric to Produce Better Network Models

December 7, 2008
Author(s)
Isabel M. Beichl, Brian D. Cloteaux
Recent research has shown that while many complex networks follow a power-law distribution for their vertex degrees, it is not sufficient to model these networks based only on their degree distribution. In order to better distinguish between these networks

Generating Network Models Using the S-Metric

July 14, 2008
Author(s)
Isabel M. Beichl, Brian D. Cloteaux
The ability to create random models of real networks is useful for understanding the interactions of the networks. Several researchers have proposed modeling of complex networks using the degree distribution, the most popular being a power-law distribution