Influence Maximization under The Non-progressive Linear Threshold Model

TL;DR

This paper extends the linear threshold model to non-progressive behavior in influence maximization, proving NP-hardness and submodularity in acyclic networks, enabling approximation algorithms for optimal influence spread.

Contribution

It introduces a non-progressive linear threshold model, analyzes its complexity, and demonstrates submodularity in directed acyclic networks, facilitating approximation solutions.

Findings

NP-hardness of influence maximization under the new model

Submodularity holds in directed acyclic networks

Approximation algorithms achieve near-optimal influence spread

Abstract

In the problem of influence maximization in information networks, the objective is to choose a set of initially active nodes subject to some budget constraints such that the expected number of active nodes over time is maximized. The linear threshold model has been introduced to study the opinion cascading behavior, for instance, the spread of products and innovations. In this paper, we we extends the classic linear threshold model [18] to capture the non-progressive be- havior. The information maximization problem under our model is proved to be NP-Hard, even for the case when the underlying network has no directed cycles. The first result of this paper is negative. In general, the objective function of the extended linear threshold model is no longer submodular, and hence the hill climbing approach that is commonly used in the existing studies is not applicable. Next, as the main result of this paper, we prove that if the underlying information network is directed acyclic, the objective function is submodular (and monotone). Therefore, in directed acyclic networks with a specified budget we can achieve 1/2 -approximation on maximizing the number of active nodes over a certain period of time by a deterministic algorithm, and achieve the (1 - 1/e )-approximation by a randomized algorithm.