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

Time-Series Graph00:54

Time-Series Graph

4.5K
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...
4.5K
Multiple Bar Graph01:07

Multiple Bar Graph

5.4K
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...
5.4K
Weighted Mean00:57

Weighted Mean

5.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.2K
Ogive Graph01:07

Ogive Graph

5.7K
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...
5.7K
Signal Flow Graphs01:18

Signal Flow Graphs

276
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
276
pV-Diagrams01:18

pV-Diagrams

4.3K
The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Aqueous-Phase Formation of Nitrogen-Containing Secondary Organic Aerosols in China Winter Haze Continuously Enhanced in the Past Decade.

Environmental science & technology·2026
Same author

miR169y-PtrNFYA6-PtrNCED3a/3b/6 module cascade regulates ABA synthesis and poplar drought tolerance.

Plant physiology·2026
Same author

Development of an interpretable machine learning model for lymphovascular space invasion prediction in patients with endometrioid endometrial carcinoma: A prospective study.

Chinese journal of cancer research = Chung-kuo yen cheng yen chiu·2026
Same author

Deletion of the D345L gene attenuates ASFV and induces protection against homologous and heterologous challenge by enhancing host innate immunity.

Emerging microbes & infections·2026
Same author

Enhanced vibration recovery of φ-OTDR under noisy circumstances using a signal-noise separation algorithm.

Optics express·2026
Same author

[Study on electrospun film-covered tracheal stents with adaptive release of anti-inflammatory drugs driven by the piezoelectric effect].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same journal

Graph Pattern Matching based reassembly - 3DGPM.

IEEE computer graphics and applications·2026
Same journal

Making Learning Visible: Turning Public Engagement into Evidence for Academic Learning.

IEEE computer graphics and applications·2026
Same journal

LlymX: Multimodal LLM-Augmented XR for Context-Aware Information Access.

IEEE computer graphics and applications·2026
Same journal

Dynamic Gaussian-Based Digital Twin Reconstruction of Articulated Multi-Joint Objects.

IEEE computer graphics and applications·2026
Same journal

Steiner and Poisson Traversal Initializations: Initial Curve Optimization for Geometric Flow-based Surface Filling.

IEEE computer graphics and applications·2026
Same journal

Insight Into the Insight Toolkit.

IEEE computer graphics and applications·2026
See all related articles

Related Experiment Video

Updated: Aug 3, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

DiffSeer: Difference-Based Dynamic Weighted Graph Visualization.

Xiaolin Wen, Yong Wang, Meixuan Wu

    IEEE Computer Graphics and Applications
    |April 8, 2023
    PubMed
    Summary
    This summary is machine-generated.

    DiffSeer visualizes dynamic weighted graphs by highlighting structural differences between time periods. This novel approach aids users in effectively analyzing temporal changes and graph evolution.

    More Related Videos

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
    07:08

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

    Published on: July 14, 2015

    7.3K
    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
    09:49

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

    Published on: September 25, 2021

    4.4K

    Related Experiment Videos

    Last Updated: Aug 3, 2025

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.2K
    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
    07:08

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

    Published on: July 14, 2015

    7.3K
    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
    09:49

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

    Published on: September 25, 2021

    4.4K

    Area of Science:

    • Computer Science
    • Information Visualization
    • Graph Theory

    Background:

    • Dynamic weighted graphs are complex to analyze due to temporal changes.
    • Existing visualization methods rely on mental comparison, limiting effective analysis of changes across time slices.

    Purpose of the Study:

    • To introduce DiffSeer, a novel approach for visualizing dynamic weighted graphs.
    • To explicitly visualize structural differences between adjacent time slices for improved analysis.

    Main Methods:

    • Developed a novel nested matrix design to overview graph structure differences and show timeslice details.
    • Implemented an optimization-based node reordering strategy to group nodes with similar evolution patterns.
    • Evaluated the approach through two case studies on real-world datasets and user interviews.

    Main Results:

    • DiffSeer effectively visualizes differences in graph structures, such as edge weight changes.
    • The nested matrix design provides both overview and detailed views of graph evolution.
    • Node reordering highlights interesting structural details and similar evolution patterns.

    Conclusions:

    • DiffSeer enhances the analysis of dynamic weighted graphs by explicitly showing temporal changes.
    • The proposed visualization approach improves user understanding of graph evolution over time.
    • User studies confirm the effectiveness of DiffSeer for dynamic weighted graph visualization.