Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

637
A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is...
637
Pipe Flowrate Measurement01:28

Pipe Flowrate Measurement

898
In pipe flow measurement, orifice, nozzle, and Venturi meters are commonly used to determine fluid flowrates by constricting the flow area, which increases fluid velocity and reduces pressure. This pressure difference, governed by Bernoulli's principle and adjusted for real-world conditions, is essential for calculating flowrate. Each meter type is suited to specific applications based on accuracy, efficiency, and compatibility with various flow conditions.
The orifice meter is a simple,...
898
Signal Flow Graphs01:18

Signal Flow Graphs

392
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
392
Review and Preview01:10

Review and Preview

8.1K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.1K
Streamlines, Streaklines, and Pathlines01:18

Streamlines, Streaklines, and Pathlines

1.6K
A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over...
1.6K
Rate-Determining Steps03:08

Rate-Determining Steps

34.3K
Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
The concept of rate-determining step can be understood from the analogy of a 4-lane freeway with a short-stretch of traffic-bottleneck caused due to...
34.3K

You might also read

Related Articles

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

Sort by
Same journal

Advancing minority stress detection with transformers: insights from the social media datasets.

Social network analysis and mining·2026
Same journal

Assessing mobile instant messenger networks with donated data.

Social network analysis and mining·2026
Same journal

Applying a panel network formation model to limited partnership matching in the private capital market.

Social network analysis and mining·2026
Same journal

C-HDNet: A Fast Hyperdimensional Computing Based Method for Causal Effect Estimation from Networked Observational Data.

Social network analysis and mining·2025
Same journal

A Bayesian mixture model for Poisson network autoregression.

Social network analysis and mining·2025
Same journal

Comparing methods for creating a national random sample of twitter users.

Social network analysis and mining·2025
See all related articles

Related Experiment Video

Updated: Nov 2, 2025

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
09:11

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Published on: April 4, 2021

3.3K

Evaluating metrics in link streams.

Frédéric Simard1

  • 1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON Canada.

Social Network Analysis and Mining
|June 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces new algorithms for analyzing temporal networks by calculating shortest fastest path metrics. These methods efficiently compute distances and latencies, offering new insights into network connectivity and centrality.

Keywords:
AlgorithmsDistancesFastest pathsLatenciesLink streamsShortest pathsTemporal networks

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K
Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.1K

Related Experiment Videos

Last Updated: Nov 2, 2025

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
09:11

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Published on: April 4, 2021

3.3K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K
Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.1K

Area of Science:

  • Network Science
  • Computer Science
  • Graph Theory

Background:

  • Temporal networks require specialized analysis for connectivity.
  • Existing methods often compute distances and latencies separately.
  • Shortest fastest paths provide novel network insights.

Purpose of the Study:

  • To develop efficient algorithms for computing shortest fastest path metrics in temporal networks.
  • To analyze the topological and temporal properties of these networks.
  • To lay the groundwork for centrality function computations on temporal networks.

Main Methods:

  • Developed four novel algorithms for temporal network analysis.
  • Two algorithms compute metrics from a fixed source node.
  • Two algorithms compute all-pairs shortest fastest path metrics, considering paths with and without delays.

Main Results:

  • Algorithms efficiently compute distances, latencies, and lengths of shortest fastest paths.
  • Proofs of correctness and temporal complexity bounds are provided.
  • Experimental results demonstrate strong performance against state-of-the-art methods on real-world datasets.

Conclusions:

  • The developed algorithms offer efficient computation of key temporal network metrics.
  • These methods enhance understanding of network connectivity and enable new centrality analyses.
  • The study contributes significant advancements to temporal network analysis.