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Related Experiment Video

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Quantifying Agonist Activity at G Protein-coupled Receptors

Quantifying Agonist Activity at G Protein-coupled Receptors

Published on: December 26, 2011

The generalized influence blocking maximization problem.

Fernando C Erd¹, André L Vignatti¹, Murilo V G da Silva¹

¹Federal University of Paraná, Curitiba, Brazil.

Social Network Analysis and Mining

|June 21, 2021

Summary

This summary is machine-generated.

This study generalizes the influence blocking maximization problem to include node costs, finding that strategies effective against misinformation depend heavily on the cost function. Simple strategies like targeting high-degree nodes or random selection show surprising effectiveness.

Keywords:

Complex networks Influence blocking maximization Misinformation

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Area of Science:

Network science
Information diffusion
Computational social science

Background:

The influence blocking maximization problem aims to minimize misinformation spread by strategically selecting nodes for a competing information campaign.
Traditional models assume uniform node costs, which may not reflect real-world scenarios.

Purpose of the Study:

To investigate a generalized influence blocking maximization problem with varying node costs.
To analyze the impact of different cost functions on the effectiveness of counter-strategies.
To explore properties and approximation results for influence functions in various diffusion models.

Main Methods:

Formulated a generalized influence blocking maximization problem with a budget constraint.
Conducted experiments comparing strategies under different cost functions (uniform vs. degree-based).
Analyzed the correlation between optimal strategies and simple heuristics (greedy degree-based, random selection).
Investigated theoretical properties and approximation algorithms for influence functions.

Main Results:

The effectiveness of influence blocking strategies significantly depends on the chosen cost function.
Strategies that perform well under uniform costs differ substantially from those optimal for degree-based costs.
Both optimal strategies, despite their differences, correlate with simple heuristics: greedy high-degree node selection and random node selection.
Demonstrated properties and approximation results for influence functions across different diffusion models.

Conclusions:

Real-world influence blocking requires cost-aware strategies that adapt to varying node costs.
Simple heuristics can be surprisingly effective, providing a practical baseline for complex problems.
Understanding influence function properties is crucial for developing robust and efficient network interventions.