Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics.

Area of Science:

• Social Network Analysis
• Game Theory
• Computational Social Science

Background:

• Influence maximization is crucial for applications like marketing and political campaigns.
• Prior research predominantly focused on discrete influence allocation (selecting a fixed number of nodes).

Purpose of the Study:

• To investigate continuous influence maximization with flexible node targeting intensity.
• To compare continuous and discrete influence maximization strategies against a passive opponent.
• To analyze the game-theoretic scenario with two active opponents.

Main Methods:

• Developed a generalized framework for continuous influence maximization.
• Analyzed optimal influence allocations in both continuous and discrete regimes.
• Investigated game-theoretic equilibria for two active opponents.

Main Results:

• Hub allocations are less critical in continuous influence maximization compared to discrete.
• Optimal continuous strategies are characterized by shadowing (targeting same nodes) and shielding (targeting opponent's neighbors).
• The unique pure Nash equilibrium for two active opponents is equal targeting of all nodes.

Conclusions:

• Continuous and discrete influence maximization exhibit fundamental differences in optimal strategies.
• Shadowing and shielding provide effective heuristics for continuous influence maximization.
• The study offers new insights into strategic influence in social networks.