Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

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...

You might also read

Related Articles

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

Sort by

Same author

Fibular flap mandibular reconstruction for third-stage medication-related osteonecrosis of the jaw: A retrospective single-center study.

Journal of dental sciences·2023

Same author

Integrated IBD Analysis, GWAS Analysis and Transcriptome Analysis to Identify the Candidate Genes for White Spot Disease in Maize.

International journal of molecular sciences·2023

Same author

Nutritional status efficacy of early nutritional support in gastrointestinal care: A systematic review and meta-analysis.

World journal of gastrointestinal surgery·2023

Same author

Unraveling the complexities of beryllium contamination in agricultural soils: The case of Qingcheng District, Qingyuan City.

Ecotoxicology and environmental safety·2023

Same author

Current and emerging applications of liquid biopsy in pan-cancer.

Translational oncology·2023

Same author

Augmented temperature fluctuation aggravates muscular atrophy through the gut microbiota.

Nature communications·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

Search research articles

Related Experiment Video

Updated: May 5, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Effective Temporal Graph Learning via Personalized PageRank.

Ziyu Liao¹, Tao Liu¹, Yue He¹

¹College of Computer and Information Science, Southwest University, Chongqing 400715, China.

Entropy (Basel, Switzerland)

|July 26, 2024

Summary

This summary is machine-generated.

This study introduces a novel temporal graph representation learning model using Time-constrained Personalized PageRank (TPPR) matrix factorization. This approach significantly enhances embedding quality for dynamic networks, outperforming existing methods on key graph learning tasks.

Keywords:

dynamic graph representation link prediction matrix factorization node classification time-constrained personalized PageRank

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Related Experiment Videos

Last Updated: May 5, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Area of Science:

Graph representation learning
Network science
Machine learning

Background:

Traditional graph representation learning methods often struggle with dynamic, large-scale networks.
Existing similarity matrix factorization techniques are primarily designed for static graphs, limiting their effectiveness in capturing evolving relationships.
The rapid growth of online interactions necessitates advanced methods for analyzing temporal graphs.

Purpose of the Study:

To develop an enhanced temporal graph representation learning model for improved embedding quality.
To leverage Time-constrained Personalized PageRank (TPPR) for better node similarity representation in dynamic graphs.
To validate the proposed model's effectiveness across various downstream tasks on temporal graph datasets.

Main Methods:

Proposed a temporal graph representation learning model based on matrix factorization of Time-constrained Personalized PageRank (TPPR) matrices.
Utilized Single Value Decomposition (SVD) or Nonnegative Matrix Factorization (NMF) to decompose TPPR matrices.
Evaluated the model on link prediction, node classification, and node clustering tasks using multiple temporal graphs.

Main Results:

The proposed TPPR matrix factorization method achieved superior performance compared to existing techniques on multiple temporal graph datasets.
Demonstrated significant improvements in embedding quality for temporal graph learning tasks.
Effectively captured node similarities in dynamic networks, leading to enhanced prediction and classification accuracy.

Conclusions:

Graph representation learning algorithms based on TPPR matrix factorization offer a powerful approach for analyzing temporal graphs.
The method provides a robust solution for incomplete similarity descriptions and low embedding quality in dynamic network analysis.
The findings highlight the effectiveness and scalability of the proposed model for real-world temporal network applications.