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

Outliers and Influential Points01:08

Outliers and Influential Points

4.1K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.1K
Work Done by Gravity01:04

Work Done by Gravity

6.8K
Gravitation is one of the four fundamental forces in nature. The force between objects on Earth and Earth itself is called gravity.
Like other forces, gravity does work on an object if it displaces it toward the Earth's center. In this case, the work done by gravity is said to be positive. If an external force acts on the object against the pull of gravity and manages to lift it away from the Earth's center, work is done against gravity. In this case, the net work done is said to be...
6.8K
Finding the Center of Gravity01:03

Finding the Center of Gravity

3.5K
The center of gravity of a body is an imaginary point where the body's total weight is assumed to be concentrated, and the body is perfectly balanced. The center of the mass of a body is a point at which the whole of the mass of the body appears to be concentrated. If the acceleration due to gravity, g, has the same value at all points on a body, its center of gravity is identical to its center of mass. The center of gravity of homogeneous bodies such as a sphere, cube, or rectangular plate...
3.5K
Center of Gravity01:15

Center of Gravity

1.5K
The center of gravity is the point at which an object's weight appears to be concentrated and can be used to balance the object perfectly. This point is essential in mechanics as it provides information regarding a body's stability and moments of inertia. The center of gravity does not always have to fall within the shape or boundaries of the body; it may also lie outside the body in certain cases.
To determine its location, the principle of moments can be utilized by dividing the...
1.5K
Gravity between Spherical Bodies01:27

Gravity between Spherical Bodies

8.5K
Newton's law of gravitation describes the gravitational force between any two point masses. However, for extended spherical objects like the Earth, the Moon, and other planets, the law holds with an assumption that masses of spherical objects are concentrated at their respective centers.
This assumption can be proved easily by showing that the expression for gravitational potential energy between a hollow sphere of mass (M) and a point mass (m) is the same as it would be for a pair of extended...
8.5K
The Principle of Superposition and the Gravitational Field01:17

The Principle of Superposition and the Gravitational Field

1.4K
The principle of superposition applies to gravitational forces of objects that are sufficiently far apart. It states that the net gravitational force on a point object is the vector sum of the gravitational forces on it due to various objects. The principle helps calculate the force by listing the individual forces and then vectorially summing them up. However, it should be noted that the principle of superposition is not always apparent. In the presence of a second force, the first force could...
1.4K

You might also read

Related Articles

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

Sort by
Same author

EAT-Lancet Diet, Plasma Metabolites, and Risk of Peripheral Artery Disease: A Prospective Cohort Study.

Journal of the American Heart Association·2026
Same author

Prevalence and determinants of academic burnout among undergraduates in a traditional Chinese medicine university: a cross-sectional study.

Frontiers in psychology·2026
Same author

Multi-scale deformable attention fusion network with global context modeling for chest X-ray lesion segmentation.

BMC medical imaging·2026
Same author

Environmentally relevant doses of fluoride exposure mediate kidney injury by affecting Tregs/Teffs homeostasis.

Chemico-biological interactions·2026
Same author

Discriminating papillary renal cell carcinoma from metanephric adenoma: a quantitative contrast-enhanced CT-based analysis.

Abdominal radiology (New York)·2026
Same author

Engineering pH-Responsive High Internal Phase Pickering Emulsions Stabilized by Copolymer Nanoparticle-Lipase Complexes for Sustainable Enzymatic Microreactors.

Langmuir : the ACS journal of surfaces and colloids·2026

Related Experiment Video

Updated: Jul 15, 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

Identifying influential nodes in complex networks using a gravity model based on the H-index method.

Siqi Zhu1, Jie Zhan2, Xing Li3

  • 1Physical and Electronic Sciences College, Hunan University of Science and Technology of China, Xiangtan, 411100, People's Republic of China. zhusiqi@mail.hnust.edu.cn.

Scientific Reports
|September 29, 2023
PubMed
Summary
This summary is machine-generated.

We introduce HVGC, a novel gravity centrality method using the H-index to identify influential spreaders in complex networks. This approach considers neighbors and network structure, outperforming existing methods in identifying key nodes.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

8.9K

Related Experiment Videos

Last Updated: Jul 15, 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
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

8.9K

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Graph Theory

Background:

  • Identifying influential nodes (spreaders) is crucial in complex networks.
  • Existing gravity-based methods often overlook the broader network context, focusing on individual node properties like degree or k-shell values.
  • This limitation hinders accurate identification of key nodes that influence network dynamics.

Purpose of the Study:

  • To propose a new gravity centrality method, HVGC (H-index based gravity centrality), for more accurate identification of influential spreaders.
  • To incorporate network topology, path information, and neighbor influence into centrality calculations.
  • To improve the detection of nodes acting as bridges, even those with lower k-shell values.

Main Methods:

  • Developed the HVGC method, integrating the H-index with gravity-based centrality concepts.
  • Incorporated analysis of neighboring node impact, inter-node path information, and node positional data.
  • Evaluated HVGC performance against existing centrality measures on ten real-world complex networks.

Main Results:

  • HVGC demonstrated superior performance in identifying influential nodes compared to traditional methods.
  • The method effectively captures the importance of bridging nodes with smaller k-shell values.
  • Experimental results on diverse real networks validate the efficacy of the proposed HVGC approach.

Conclusions:

  • The HVGC method offers a more comprehensive and accurate approach to identifying influential spreaders in complex networks.
  • Considering network structure and neighbor influence provides a significant advantage over methods relying solely on individual node metrics.
  • HVGC advances the field of network science by providing a refined tool for network analysis and understanding information diffusion.