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

6.8K
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...
6.8K
Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

154
The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
154
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

58
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
58
Methods of Medium Optimization01:28

Methods of Medium Optimization

58
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
58
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

721
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
721
Pore Size Distribution01:23

Pore Size Distribution

619
In concrete, the pore size distribution significantly influences the material's properties. Capillary pores, markedly larger than gel pores, form a vast network within partially hydrated cement paste, reducing the concrete's strength and increasing its permeability. This heightened permeability leads to a greater risk of damage from environmental factors like freeze-thaw cycles and chemical attacks, with the extent of vulnerability also being tied to the water-to-cement ratio.
Adequate...
619

You might also read

Related Articles

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

Sort by
Same author

The role of fibration symmetries in geometric deep learning.

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

Generalized Probabilistic Approximate Optimization Algorithm.

Nature communications·2025
Same author

The k-core as a predictor of structural collapse in mutualistic ecosystems.

Nature physics·2025
Same author

Cognitive distortions are associated with increasing political polarization.

Communications psychology·2025
Same author

High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.

Radiology. Artificial intelligence·2025
Same author

Stabilization of recurrent neural networks through divisive normalization.

bioRxiv : the preprint server for biology·2025
Same journal

Harmonizing standards and resources for the medical genome.

Nature·2026
Same journal

Towards the construction of a virtual yeast.

Nature·2026
Same journal

Aerosols and hydrocarbons in the atmosphere of a white dwarf planet.

Nature·2026
Same journal

TROP2 targeting reveals therapy-driven cell state dynamics in colorectal cancer.

Nature·2026
Same journal

Competing programs shape cortical sensorimotor-association axis development.

Nature·2026
Same journal

Steatosis shapes prognosis-defining liver metastasis heterogeneity in CRC.

Nature·2026
See all related articles

Related Experiment Video

Updated: Apr 7, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Influence maximization in complex networks through optimal percolation.

Flaviano Morone1, Hernán A Makse1

  • 1Levich Institute and Physics Department, City College of New York, New York, New York 10031, USA.

Nature
|July 2, 2015
PubMed
Summary
This summary is machine-generated.

Identifying key influencers in complex networks is crucial for information spread and epidemic prevention. This study reveals a minimal set of optimal influencers, often including overlooked low-degree nodes, using a novel network science approach.

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

1.7K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.4K

Related Experiment Videos

Last Updated: Apr 7, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
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

1.7K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.4K

Area of Science:

  • Network Science
  • Statistical Physics
  • Complex Systems

Background:

  • Identifying influential nodes is critical for understanding information diffusion and epidemic spread in complex networks.
  • Current heuristic strategies for finding these key nodes are often insufficient and fail to identify the truly optimal set.
  • The problem of finding minimal sets of influencers remains a significant challenge in network science.

Purpose of the Study:

  • To develop a theoretical framework for identifying the minimal set of influential nodes in complex networks.
  • To map the influencer localization problem onto optimal percolation in random networks.
  • To uncover previously neglected nodes that function as optimal influencers.

Main Methods:

  • Mapping the problem to optimal percolation in random networks.
  • Minimizing the energy of a many-body system with interactions defined by the network's non-backtracking matrix.
  • Utilizing big data analyses to validate findings.

Main Results:

  • The identified set of optimal influencers is significantly smaller than predicted by traditional centrality measures.
  • A substantial number of weakly connected, low-degree nodes, previously overlooked, emerge as crucial influencers.
  • These optimal influencers are characterized by hierarchical structures, with low-degree nodes surrounded by hubs.

Conclusions:

  • The novel approach provides a more accurate method for identifying minimal sets of influencers in complex networks.
  • The findings challenge existing centrality-based heuristics by highlighting the importance of specific low-degree nodes.
  • The theoretical framework offers potential universality for solving other complex optimization problems.