Manipulating concept spread using concept relationships

Area of Science:

• Social network analysis
• Computational social science
• Information diffusion modeling

Background:

• Influence spread in populations is modeled as cascades from initial agents.
• Identifying influential individuals has applications in epidemic control, marketing, and social norms.
• Real-world concept spread involves interactions that can boost or inhibit propagation.

Purpose of the Study:

• To address the lack of consideration for concept interactions in previous influence spread models.
• To enable indirect concept manipulation by leveraging interactions between concepts.
• To propose and evaluate heuristics for boosting or inhibiting concept spread in interactive environments.

Main Methods:

• Development of a framework to represent interactions between multiple concepts.
• Proposal of the 'maximum probable gain' heuristic to identify influential paths for concept manipulation.
• Comparison of the proposed heuristic against established methods for influence spread manipulation.

Main Results:

• The 'maximum probable gain' heuristic demonstrates effectiveness in indirectly manipulating concept spread.
• The framework successfully models complex interactions between numerous concepts.
• Established heuristics were used as a benchmark for performance comparison.

Conclusions:

• Considering concept interactions is crucial for effective indirect concept manipulation.
• The 'maximum probable gain' heuristic offers a promising approach for controlling influence spread.
• This research opens new avenues for managing information diffusion in complex social systems.