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

4.6K
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...
4.6K
Ogive Graph01:07

Ogive Graph

6.1K
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...
6.1K
Bar Graph01:07

Bar Graph

20.3K
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...
20.3K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

15.3K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
15.3K
Run Charts01:12

Run Charts

133
Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
133
Multiple Bar Graph01:07

Multiple Bar Graph

8.3K
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...
8.3K

You might also read

Related Articles

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

Sort by
Same author

Theoretical investigation of catalytic oxidation of benzyl alcohol by Au, Cu and Au-Cu nanoclusters.

Physical chemistry chemical physics : PCCP·2026
Same author

Prevalence of Hypovitaminosis D Among Patients With Type 2 Diabetes Mellitus: Evidence From a Diabetes Clinic in Durgapur, West Bengal.

Cureus·2026
Same author

Is range separation always necessary for modeling TADF emitters? A benchmark study of B3LYP on dispersion-corrected geometries <i>vs.</i> tuned ωB97X-D.

Physical chemistry chemical physics : PCCP·2026
Same author

Structurally Resolved Water-Soluble Copper Nanoclusters with NIR TADF Exhibiting Multifunctional Catalytic Activity.

The journal of physical chemistry letters·2026
Same author

Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.

ACS omega·2026
Same author

Overcoming the bottleneck of d-band holes in plasmonic photocatalysis through molecular electronic coupling.

Nanoscale·2026
Same journal

Identifying clinical feature clusters toward predicting stroke in patients with asymptomatic carotid stenosis.

International journal of data science and analytics·2025
Same journal

Narratives from GPT-derived networks of news and a link to financial markets dislocations.

International journal of data science and analytics·2025
Same journal

Analyzing international airtime top-up transfers for migration and mobility.

International journal of data science and analytics·2023
Same journal

Evaluating narrative visualization: a survey of practitioners.

International journal of data science and analytics·2023
Same journal

Fake news detection: deep semantic representation with enhanced feature engineering.

International journal of data science and analytics·2023
Same journal

AI and data science for smart emergency, crisis and disaster resilience.

International journal of data science and analytics·2023
See all related articles

Related Experiment Video

Updated: Oct 11, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.5K

Mining subgraph coverage patterns from graph transactions.

A Srinivas Reddy1, P Krishna Reddy1, Anirban Mondal2

  • 1Kohli Centre on Intelligent Systems, IIIT, Hyderabad, India.

International Journal of Data Science and Analytics
|December 7, 2021
PubMed
Summary
This summary is machine-generated.

We introduce subgraph coverage patterns (SCPs) for analyzing graph transactional data (GTD). Our SIFT framework efficiently extracts SCPs, demonstrating value in drug design.

Keywords:
Bio-informaticsGraph miningSubgraph coverage patternsSubgraph mining

More Related Videos

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

696
Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

2.0K

Related Experiment Videos

Last Updated: Oct 11, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.5K
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

696
Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

2.0K

Area of Science:

  • Graph mining
  • Bioinformatics
  • Chemical informatics
  • Social network analysis

Background:

  • Graph transactional data (GTD) mining is crucial for various scientific domains.
  • Existing methods focus on frequent subgraph mining, overlooking coverage aspects.
  • Subgraph coverage patterns (SCPs) offer valuable insights beyond simple frequency.

Purpose of the Study:

  • To introduce the novel concept of subgraph coverage patterns (SCPs).
  • To develop an efficient framework for extracting SCPs from GTD.
  • To evaluate the framework's performance and demonstrate its utility in applications like drug design.

Main Methods:

  • Definition of subgraph coverage patterns (SCPs) with user-defined constraints (frequency, coverage, overlap).
  • Development of the Subgraph ID-based Flat Transactional (SIFT) framework for efficient SCP extraction.
  • Performance evaluation on three real-world datasets.

Main Results:

  • The SIFT framework efficiently extracts SCPs from GTD.
  • Empirical evaluation on real datasets confirms the framework's capability.
  • A case study in computer-aided drug design highlights the practical effectiveness of SIFT.

Conclusions:

  • The proposed SIFT framework provides an efficient solution for mining subgraph coverage patterns.
  • SCPs offer valuable knowledge complementary to frequent subgraph mining.
  • SIFT demonstrates significant potential for applications in bioinformatics, chemical informatics, and drug design.