Time-Bounded Influence Diffusion with Incentives

TL;DR

This paper extends influence diffusion models by incorporating incentives to lower node thresholds, aiming to minimize total incentives for complete influence within a set number of rounds, with efficient algorithms for specific network types.

Contribution

It introduces a new influence diffusion model with incentives and provides polynomial-time algorithms for paths, trees, and complete networks.

Findings

Polynomial algorithms for paths, trees, and complete networks.

Incentive-based threshold reduction effectively accelerates influence spread.

Hard to approximate in general networks.

Abstract

A widely studied model of influence diffusion in social networks represents the network as a graph $G=(V,E)$ with an influence threshold $t(v)$ for each node. Initially the members of an initial set $S\subseteq V$ are influenced. During each subsequent round, the set of influenced nodes is augmented by including every node $v$ that has at least $t(v)$ previously influenced neighbours. The general problem is to find a small initial set that influences the whole network. In this paper we extend this model by using \emph{incentives} to reduce the thresholds of some nodes. The goal is to minimize the total of the incentives required to ensure that the process completes within a given number of rounds. The problem is hard to approximate in general networks. We present polynomial-time algorithms for paths, trees, and complete networks.