Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Social Proof

Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Theory of Attribution I: Correspondent Inference Theory

Theory of Attribution I: Correspondent Inference Theory

Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...

Social Exchange Theory

Social Exchange Theory

We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).

Social Exchange Theory

Social Exchange Theory

As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...

You might also read

Related Articles

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

Sort by

Same author

The implicit regularizing effect of stochastic resetting in deep learning analysis of anomalous diffusion.

Chaos (Woodbury, N.Y.)·2026

Same author

Membrane voltage multistability in coupled glial cells.

bioRxiv : the preprint server for biology·2026

Same author

HARL-TRADE: A hierarchical adaptive reinforcement learning framework for second-level high-frequency trading.

Chaos (Woodbury, N.Y.)·2026

Same author

Quantifying the temporal dynamics of environmental awareness through longitudinal social media analysis.

Chaos (Woodbury, N.Y.)·2026

Same author

AI-driven landscape values mapping.

Chaos (Woodbury, N.Y.)·2026

Same author

Shear-driven finite-velocity diffusion and its generalization.

Chaos (Woodbury, N.Y.)·2026

Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016

See all related articles

Search research articles

Related Experiment Videos

Model for rumor spreading over networks.

Daniel Trpevski¹, Wallace K S Tang, Ljupco Kocarev

¹Macedonian Academy for Sciences and Arts, Skopje, Macedonia.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 28, 2010

Summary

This summary is machine-generated.

In networks, a preferred rumor (rumor 1) dominates when nodes have high connectivity or clustering. However, rumor 2 can still spread under specific network conditions, like moderate clustering or low average degree.

Related Experiment Videos

Area of Science:

Network Science
Information Diffusion
Computational Social Science

Background:

Introduces a novel model for rumor propagation, extending the susceptible-infective-susceptible (SIS) model.
Considers two distinct rumors with varying acceptance probabilities and asymmetric adoption preferences.

Purpose of the Study:

To investigate the conditions under which one rumor dominates another in a network.
To analyze the impact of network topology on rumor spreading dynamics.

Main Methods:

Developed a generalized rumor spreading model with asymmetric adoption.
Conducted numerical simulations on synthetic networks: Watts-Strogatz, Erdos-Renyi, and Barabasi-Albert models.

Main Results:

Rumor 1 dominates in high-degree or highly clustered networks.
Rumor 2 can persist in specific network configurations, including moderate clustering (Watts-Strogatz), low average degree (Erdos-Renyi), and low link addition (Barabasi-Albert).

Conclusions:

Network topology significantly influences which rumor prevails.
The model provides insights into information competition and spread in complex networks.