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

Ogive Graph01:07

Ogive Graph

6.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.8K
Graphing Antiderivatives01:30

Graphing Antiderivatives

76
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
76
Graphs of Functions01:30

Graphs of Functions

354
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
354
Bar Graph01:07

Bar Graph

23.1K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.1K
Time-Series Graph00:54

Time-Series Graph

5.2K
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...
5.2K
Multiple Bar Graph01:07

Multiple Bar Graph

10.2K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.2K

You might also read

Related Articles

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

Sort by
Same author

Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Framework for <i>de novo</i> sequencing of peptide mixtures <i>via</i> network analysis and two-dimensional tandem mass spectrometry.

Chemical science·2025
Same author

A large-scale proteogenomic atlas of pear.

Molecular plant·2023
Same author

Significantly enhanced photothermal catalytic CO<sub>2</sub> reduction over TiO<sub>2</sub>/g-C<sub>3</sub>N<sub>4</sub> composite with full spectrum solar light.

Journal of colloid and interface science·2023
Same author

High-power diode-end-pumped 1314 nm laser based on the multi-segmented Nd:YLF crystal.

Optics letters·2023
Same author

Hybrid peptide NTP-217 triggers ROS-mediated rapid necrosis in liver cancer cells by induction of mitochondrial leakage.

Frontiers in oncology·2023
Same journal

A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2026
Same journal

Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
10:07

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

Published on: February 10, 2015

20.0K

Local Higher-Order Graph Clustering.

Hao Yin1, Austin R Benson1, Jure Leskovec1

  • 1Stanford University.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|May 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new local graph clustering method, Motif-based Approximate Personalized PageRank (MAPPR), that effectively uses network motifs to improve community detection in complex networks. MAPPR outperforms existing methods by considering higher-order structures and directed networks.

More Related Videos

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.8K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.7K

Related Experiment Videos

Last Updated: Feb 10, 2026

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
10:07

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

Published on: February 10, 2015

20.0K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.8K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.7K

Area of Science:

  • Graph theory
  • Network science
  • Data mining

Background:

  • Local graph clustering methods offer efficient, targeted node clustering.
  • Existing methods struggle with higher-order network structures and directed graphs.

Purpose of the Study:

  • To develop a novel local graph clustering method that incorporates higher-order network information.
  • To address limitations of current methods in handling complex network structures and directed graphs.

Main Methods:

  • Introduced the Motif-based Approximate Personalized PageRank (MAPPR) algorithm.
  • Incorporated network motifs (small subgraphs) to capture higher-order structures.
  • Generalized conductance metric to 'motif conductance' for cluster quality evaluation.

Main Results:

  • MAPPR demonstrates a fast running time independent of graph size.
  • Theoretical guarantees on cluster quality were established using motif conductance.
  • Experimental results show MAPPR outperforms edge-based Personalized PageRank in community detection.

Conclusions:

  • MAPPR provides an effective framework for local graph clustering by leveraging network motifs.
  • The method enhances community detection accuracy, especially in networks with complex structures.
  • MAPPR offers a significant advancement over existing local graph clustering techniques.