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

Transformations of Functions III01:20

Transformations of Functions III

174
Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...
174
Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.4K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

523
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
523
The Ideal Transformer01:26

The Ideal Transformer

1.4K
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
1.4K
Transformations of Functions I01:29

Transformations of Functions I

176
A function's graph can be modified by changing its position or size without altering its overall shape. These transformations allow the graph to be moved across the coordinate plane while preserving its pattern and structure. One of the most common transformations is shifting, which repositions the graph without distorting it.When the output of a function is adjusted by adding or subtracting a constant, the graph shifts vertically. A positive value moves the graph upward, while a negative value...
176
Transformers01:26

Transformers

1.7K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.7K

You might also read

Related Articles

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

Sort by
Same author

The signed two-space proximity model for learning representations in protein-protein interaction networks.

Bioinformatics (Oxford, England)·2025
Same author

Bitcoin research with a transaction graph dataset.

Scientific data·2025
Same author

Explainable Multilayer Graph Neural Network for cancer gene prediction.

Bioinformatics (Oxford, England)·2023
Same author

Synthetic electronic health records generated with variational graph autoencoders.

NPJ digital medicine·2023
Same author

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.

Scientific reports·2023
Same author

Modularity-aware graph autoencoders for joint community detection and link prediction.

Neural networks : the official journal of the International Neural Network Society·2022

Related Experiment Video

Updated: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

On the theoretical expressive power of graph transformers for solving graph problems.

Giannis Nikolentzos1, Dimitrios Kelesis2, Michalis Vazirgiannis3

  • 1Department of Informatics and Telecommunications, University of Peloponnese, Akadimaikou G.K. Vlachou Street, Tripoli, 22131, Greece.

Neural Networks : the Official Journal of the International Neural Network Society
|September 21, 2025
PubMed
Summary

Graph Transformers, a new neural network architecture, show Turing universality and can solve problems beyond the capabilities of Graph Neural Networks (GNNs). These findings highlight their potential in graph-based machine learning tasks.

Keywords:
Congested cliqueExpressive powerGraph problemsGraph transformers

More Related Videos

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

424

Related Experiment Videos

Last Updated: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

424

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Graph Neural Networks

Background:

  • Transformers dominate NLP and computer vision.
  • Graph Transformers (GTs) are emerging alternatives to message passing Graph Neural Networks (MPNNs).
  • The expressive power of GTs is underexplored, unlike MPNNs.

Purpose of the Study:

  • To understand the strengths and limitations of Graph Transformer architectures.
  • To connect Graph Transformers to the Congested Clique model from distributed computing.
  • To analyze the theoretical capabilities of Graph Transformers.

Main Methods:

  • Derived a connection between Graph Transformers and the Congested Clique model.
  • Translated theoretical results from distributed computing to Graph Transformers.
  • Empirically evaluated Graph Transformers and MPNNs on molecular datasets.

Main Results:

  • Graph Transformers with depth 2 demonstrate Turing universality under certain conditions.
  • Identified Graph Transformers capable of solving problems intractable for MPNNs.
  • Empirical results show GTs effectively address molecular graph tasks where MPNNs fail.

Conclusions:

  • Graph Transformers possess greater expressive power than MPNNs.
  • Depth 2 Graph Transformers are Turing universal, offering significant computational capabilities.
  • GTs show promise for complex graph-based machine learning problems, particularly in chemistry.