Adaptive Submodular Influence Maximization with Myopic Feedback

TL;DR

This paper introduces a new adaptive influence maximization strategy in social networks that uses myopic feedback and guarantees near-optimal performance, validated through empirical analysis.

Contribution

It proposes the myopic adaptive greedy policy that maximizes a novel utility function, providing theoretical approximation guarantees under a diffusion model.

Findings

The strategy achieves a (1 - 1/e)-approximation of the optimal policy.

Empirical results on real-world networks validate the effectiveness of the approach.

The utility function considers cumulative active nodes over time, not just final count.

Abstract

This paper examines the problem of adaptive influence maximization in social networks. As adaptive decision making is a time-critical task, a realistic feedback model has been considered, called myopic. In this direction, we propose the myopic adaptive greedy policy that is guaranteed to provide a (1 - 1/e)-approximation of the optimal policy under a variant of the independent cascade diffusion model. This strategy maximizes an alternative utility function that has been proven to be adaptive monotone and adaptive submodular. The proposed utility function considers the cumulative number of active nodes through the time, instead of the total number of the active nodes at the end of the diffusion. Our empirical analysis on real-world social networks reveals the benefits of the proposed myopic strategy, validating our theoretical results.