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

¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.2K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.2K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.6K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.9K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.9K
Protein Networks02:26

Protein Networks

4.2K
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,...
4.2K
Unsymmetric Bending01:18

Unsymmetric Bending

572
Unsymmetrical bending occurs when the bending moment applied to a structural member does not align with its principal axis. This misalignment leads to complex stress distributions and deflection patterns that differ from those in symmetrical bending, and are essential for designing structures to withstand different loading conditions. In unsymmetrical bending, the neutral axis—where stress is zero—does not necessarily align with the geometric axes of the cross-section. The...
572
Nodal Analysis01:10

Nodal Analysis

1.3K
Nodal analysis is a fundamental method in electrical engineering used to simplify the process of circuit analysis. This method revolves around the concept of using node voltages as the primary variables for circuit analysis. The objective is to determine the voltage at each node in a circuit, which can then be used to find other quantities of interest, such as currents through specific components.
Consider, for instance, a simple circuit composed of three nodes and three resistors, as shown in...
1.3K

You might also read

Related Articles

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

Sort by
Same author

SuperARC: a test for artificial superintelligence based on compressed modelling, recursive prediction and problem complexity.

Nature communications·2026
Same author

Neurodivergent influenceability in agentic AI as a contingent solution to the AI alignment problem.

PNAS nexus·2026
Same author

Leveraging network motifs to improve artificial neural networks.

Nature communications·2025
Same author

Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.

Journal of the Royal Society, Interface·2025
Same author

On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures.

NPJ systems biology and applications·2024
Same author

PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.

Bioinformatics advances·2023
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 30, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K

Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks.

Felipe S Abrahão1,2, Klaus Wehmuth1, Hector Zenil2,3,4,5

  • 1National Laboratory for Scientific Computing (LNCC), Petropolis 25651-075, RJ, Brazil.

Entropy (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

Algorithmic information distortions in multidimensional networks can grow exponentially with extra dimensions, especially in node-unaligned cases. Isomorphisms do not preserve algorithmic information, requiring analysis of the multidimensional space itself.

Keywords:
algorithmic complexitygraph isomorphisminformation content analysisinformation distortionlossless compressionmultiaspect graphsmultidimensional networksmultilayer networksnetwork complexity

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.7K

Related Experiment Videos

Last Updated: Oct 30, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.7K

Area of Science:

  • Network Science
  • Information Theory
  • Complexity Science

Background:

  • Algorithmic information theory quantifies information in data using lossless compressibility.
  • Monoplex networks are single-layered, while multidimensional networks possess multiple layers or aspects.
  • Importing methods from monoplex to multidimensional networks can introduce information distortions.

Purpose of the Study:

  • To investigate the limitations of applying algorithmic information theory methods from monoplex to multidimensional networks.
  • To analyze how algorithmic information distortions scale with the number of dimensions in various multidimensional network configurations.
  • To establish conditions under which algorithmic information is preserved or lost during network isomorphism.

Main Methods:

  • Theoretical analysis of algorithmic information distortions in node-aligned and node-unaligned multidimensional networks.
  • Comparison of distortions in uniform and non-uniform multidimensional spaces.
  • Mathematical derivation of distortion growth rates with respect to the number of extra dimensions.

Main Results:

  • Node-unaligned multidimensional networks (uniform or non-uniform spaces) exhibit exponentially increasing algorithmic information distortions with more dimensions.
  • Node-aligned multidimensional networks with uniform spaces show distortions growing only logarithmically with extra dimensions.
  • Algorithmic information distortions can grow exponentially in node-unaligned multidimensional networks, unlike node-aligned ones.

Conclusions:

  • Isomorphisms between monoplex and multidimensional networks generally do not preserve algorithmic information.
  • The inherent algorithmic information of the multidimensional space is crucial for accurate complexity analysis.
  • Understanding these distortions is vital for applying information-theoretic measures to complex, multilayered network structures.