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

Survival Tree01:19

Survival Tree

358
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
358
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
14.4K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.1K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.1K
Protein Networks02:26

Protein Networks

4.4K
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,...
4.4K

You might also read

Related Articles

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

Sort by
Same author

Three new iridoid glycosides from the roots of <i>Nardostachys jatamansi</i>.

Journal of Asian natural products research·2026
Same author

Three new compounds from <i>Nardostachys jatamansi</i>.

Journal of Asian natural products research·2026
Same author

Antibacterial Mechanism of Dipicolinic Acid Against <i>Xanthomonas citri</i> pv. <i>glycines</i> and Its Efficacy for the Management of Soybean Bacterial Pustule Disease.

Biomolecules·2026
Same author

Clinical, neuroimaging, and biomarker profiling of four Alzheimer's disease pedigrees caused by pathogenic APP variants.

Alzheimer's research & therapy·2026
Same author

Mental Health Literacy and Associated Factors in Ethnic Minority Border Regions of Guangxi, China: A Cross-Sectional Study.

Risk management and healthcare policy·2026
Same author

Roles of spoVF operon subunits A and B (dipicolinic acid synthase) in regulating cell morphology and biofilm formation in the biocontrol agent Bacillus subtilis.

Pest management science·2026
Same journal

Multiscale dynamics of special memristive ion channels in a neural circuit.

Chaos (Woodbury, N.Y.)·2026
Same journal

Symmetry-protected delay spectroscopy in oscillator networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Mesoscale community organization governs epidemic onset and spread in metapopulations.

Chaos (Woodbury, N.Y.)·2026
Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jan 4, 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

1.2K

Network embedding for link prediction: The pitfall and improvement.

Ren-Meng Cao1, Si-Yuan Liu1, Xiao-Ke Xu1

  • 1College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China.

Chaos (Woodbury, N.Y.)
|November 3, 2019
PubMed
Summary
This summary is machine-generated.

Network embedding methods struggle with short-path networks. A new link prediction approach combining embedding with local structure significantly improves performance, especially in these challenging networks.

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

530
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

956

Related Experiment Videos

Last Updated: Jan 4, 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

1.2K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

530
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

956

Area of Science:

  • Graph theory
  • Machine learning
  • Network science

Background:

  • Link prediction is crucial for complex network analysis.
  • Existing methods fall into structural similarity and network embedding categories.
  • The relationship and comparative performance of these categories are underexplored.

Purpose of the Study:

  • To systematically compare structural similarity and network embedding algorithms for link prediction.
  • To investigate the limitations of network embedding, particularly in short-path networks.
  • To propose an improved link prediction method addressing these limitations.

Main Methods:

  • Comparative analysis of structural similarity and network embedding algorithms.
  • Analysis of Euclidean distance distribution in embedded vector spaces for short-path networks.
  • Development of a novel link prediction method integrating local structural information with network embedding.

Main Results:

  • Network embedding algorithms exhibit poor performance on short-path networks due to indistinguishable distance distributions.
  • Structural similarity algorithms perform better in short-path networks.
  • The proposed hybrid method significantly enhances link prediction accuracy, especially for short-path networks.

Conclusions:

  • Network embedding's effectiveness is limited in networks with short paths.
  • Integrating local structural information into network embedding is a viable strategy to improve link prediction.
  • The proposed method offers substantial performance gains in empirical network analysis.