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

Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.3K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.3K
Random Error01:04

Random Error

9.9K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.9K
Random Variables01:09

Random Variables

17.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.9K
Randomized Experiments01:13

Randomized Experiments

9.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.1K
Random and Systematic Errors01:20

Random and Systematic Errors

15.4K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.4K

You might also read

Related Articles

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

Sort by
Same author

Electrophoretic Molecular Communication With Piecewise Constant Electric Field.

IEEE transactions on nanobioscience·2022
Same author

Kolmogorov Basic Graphs and Their Application in Network Complexity Analysis.

Entropy (Basel, Switzerland)·2021
Same author

Relationship between Age and Value of Information for a Noisy Ornstein-Uhlenbeck Process.

Entropy (Basel, Switzerland)·2021
Same author

Entropy of spatial network ensembles.

Physical review. E·2018
Same author

Comparative evaluation of the stability of two different dental implant designs and surgical protocols-a pilot study.

International journal of implant dentistry·2017
Same author

Immediate and Early Loading of Hydrothermally Treated, Hydroxyapatite-Coated Dental Implants: 2-Year Results from a Prospective Clinical Study.

The Journal of oral implantology·2015
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Feb 15, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.6K

Accessibility and delay in random temporal networks.

Shahriar Etemadi Tajbakhsh1, Justin P Coon1, David E Simmons1

  • 1Department of Engineering Science, University of Oxford, Oxford, United Kingdom.

Physical Review. E
|January 20, 2018
PubMed
Summary
This summary is machine-generated.

This study analyzes random temporal networks, providing bounds for node accessibility probability. An exact probability is derived for networks with uniform link probabilities, enhancing path analysis in dynamic systems.

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.0K
Anteromesial Temporal Lobectomy for Medically Intractable Temporal Lobe Epilepsy: An Operative Study
11:29

Anteromesial Temporal Lobectomy for Medically Intractable Temporal Lobe Epilepsy: An Operative Study

Published on: August 15, 2025

2.5K

Related Experiment Videos

Last Updated: Feb 15, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.6K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.0K
Anteromesial Temporal Lobectomy for Medically Intractable Temporal Lobe Epilepsy: An Operative Study
11:29

Anteromesial Temporal Lobectomy for Medically Intractable Temporal Lobe Epilepsy: An Operative Study

Published on: August 15, 2025

2.5K

Area of Science:

  • Network Science
  • Probability Theory
  • Complex Systems Analysis

Background:

  • Real-world networks often exhibit temporal links that appear and disappear randomly.
  • Understanding path formation in these dynamic networks is crucial for many applications.
  • The random nature of link existence presents significant analytical challenges.

Purpose of the Study:

  • To formulate the probability of accessibility between nodes in random temporal networks.
  • To develop computationally tractable bounds for this probability.
  • To extend the analysis to networks with continuous probability distributions for link durations.

Main Methods:

  • Microscopic analysis of random temporal networks.
  • Derivation of upper and two lower bounds for accessibility probability.
  • Exact probability calculation for networks with uniform link probabilities.
  • Method for transforming continuous probability distributions to discrete probabilities.

Main Results:

  • Established bounds for accessibility probability in general random temporal networks.
  • Obtained the exact accessibility probability for a special case with uniform link probabilities.
  • Developed a transformation method for continuous link duration distributions.

Conclusions:

  • The study provides valuable analytical tools for understanding connectivity in random temporal networks.
  • The derived bounds and exact probabilities offer insights into network dynamics.
  • The transformation method broadens the applicability of the findings to diverse network models.