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Related Concept Videos

Persuasion Strategies01:52

Persuasion Strategies

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Researchers have tested many persuasion strategies, including the foot-in-the door and the door-in-the-face techniques, in a variety of contexts. Ultimately, the principles are effective in selling products and changing people’s attitude, ideas, and behaviors (Cialdini & Goldstein, 2004).
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Routes of Persuasion02:20

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Persuasion is the process of changing our attitude toward something based on some kind of communication. Much of the persuasion we experience comes from outside forces. How do people convince others to change their attitudes, beliefs, and behaviors? What communications do you receive that attempt to persuade you to change your attitudes, beliefs, and behaviors?
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Conformity01:20

Conformity

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Conformity is the change in a person’s behavior to go along with the group, even if that person does not agree with the group.
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Social Proof00:52

Social Proof

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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.
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Relationship Formation02:12

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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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Related Experiment Video

Updated: Jun 27, 2025

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

Published on: December 7, 2021

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Individual-centralized seeding strategy for influence maximization in information-limited networks.

Yang Liu1, Xiaoqi Wang2, Xi Wang3,4

  • 1School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University , Xi'an, 710072, China.

Journal of the Royal Society, Interface
|May 8, 2024
PubMed
Summary

This study introduces a new method to identify influential individuals in networks for better information dissemination. The approach uses respondent nominations to find key people, improving upon existing seeding strategies.

Keywords:
complex networksinfluence maximizationinformation-limited networksspreading dynamics

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Area of Science:

  • Social Network Analysis
  • Information Diffusion Studies
  • Behavioral Economics

Background:

  • Peer effects drive behavior through interaction networks, amplified by targeting influential individuals.
  • Current seeding strategies like the one-hop method face challenges with impractical or expensive network data acquisition.
  • Information-limited networks hinder effective targeting of key individuals for interventions.

Purpose of the Study:

  • To propose an individual-centralized seeding strategy for identifying influential individuals in information-limited networks.
  • To develop a method that infers network structure through respondent nominations, reducing data collection costs.
  • To enhance the effectiveness of seeding strategies in real-world field studies and interventions.

Main Methods:

  • An individual-centralized approach using follow-up questions (e.g., 'Who has more connections/friends?').
  • Constructing a seeding set based on nodes receiving the most nominations from respondents.
  • Evaluating the strategy's performance on diverse experimental network datasets.

Main Results:

  • The proposed method successfully identifies more influential seeds compared to the one-hop strategy.
  • Nomination-based inference acquires significant network information without surveying additional individuals.
  • The strategy demonstrates superior performance over existing methods in various network settings.

Conclusions:

  • The individual-centralized seeding approach is effective for targeting influential individuals in information-limited networks.
  • This method offers a practical and cost-efficient alternative to traditional network surveying for seeding strategies.
  • The proposed approach holds potential for improving the efficacy of interventions in real-world scenarios.