Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Graphical Representation of Inequalities

Graphical Representation of Inequalities

The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...

Vector Algebra: Graphical Method

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

Graphs of Equations in Two Variables

Graphs of Equations in Two Variables

An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...

Fast Decoupled and DC Powerflow

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:

Graphs of Functions

Graphs of Functions

Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach.

medRxiv : the preprint server for health sciences·2026

Same author

A health and economic evaluation of the spatial spillover effect from measles resurgence.

Scientific reports·2025

Same author

Scenario Projections of COVID-19 Burden in the US, 2024-2025.

JAMA network open·2025

Same author

The impact of risk compensation adaptive behavior on the final epidemic size.

Mathematical biosciences·2025

Same author

Epihiper-A high performance computational modeling framework to support epidemic science.

PNAS nexus·2024

Same author

A simple model of coupled individual behavior and its impact on epidemic dynamics.

Mathematical biosciences·2024

Same journal

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread.

Journal of parallel and distributed computing·2024

Same journal

Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data.

Journal of parallel and distributed computing·2021

Same journal

Fast GPU 3D diffeomorphic image registration.

Journal of parallel and distributed computing·2020

Same journal

Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent.

Journal of parallel and distributed computing·2020

Same journal

Modeling and analysis of epidemic spreading on community networks with heterogeneity.

Journal of parallel and distributed computing·2020

Same journal

Towards High Performance Data Analytic on Heterogeneous Many-core Systems: A Study on Bayesian Sequential Partitioning.

Journal of parallel and distributed computing·2019

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 25, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Parallel Algorithms for Switching Edges in Heterogeneous Graphs.

Hasanuzzaman Bhuiyan^1,2, Maleq Khan³, Jiangzhuo Chen²

¹Department of Computer Science, Virginia Tech, 2202 Kraft Drive, Blacksburg, VA 24061, USA.

Journal of Parallel and Distributed Computing

|August 1, 2017

Summary

This summary is machine-generated.

We developed efficient parallel algorithms for edge switching in massive networks, achieving significant speedups. Our methods enable faster analysis of complex networks and parallel computation of multinomial random variables.

Keywords:

edge switch multinomial distribution network dynamics parallel algorithms random network generation

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

Related Experiment Videos

Last Updated: Feb 25, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

Area of Science:

Graph Theory and Network Analysis
Parallel Computing

Background:

Edge switching is crucial for random network generation and dynamic network analysis.
Designing parallel algorithms for edge switching faces challenges due to dependencies and maintaining graph simplicity.

Purpose of the Study:

To present efficient distributed memory parallel algorithms for edge switching in massive networks.
To address challenges in parallelizing edge switch operations for improved scalability.

Main Methods:

Developed distributed memory parallel algorithms for edge switching.
Introduced the first non-trivial parallel algorithm for computing multinomial random variables.

Main Results:

Achieved good speedup and scalability on large networks.
Demonstrated a harmonic mean speedup of 73.25 with 1024 processors.
Obtained a speedup of 925 for parallel multinomial random variable computation using 1024 processors.

Conclusions:

The presented parallel algorithms offer efficient solutions for edge switching in massive networks.
The algorithms scale well, providing significant speedups and enabling advanced network analysis.