LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection

TL;DR

This paper introduces LOFA, an online influence maximization algorithm that effectively leverages submodularity under full-bandit feedback, demonstrating superior empirical performance on real social network data.

Contribution

The paper proposes LOFA, a novel lazy forward selection algorithm for online influence maximization under full-bandit feedback, achieving lower regret than existing methods.

Findings

LOFA outperforms existing algorithms in cumulative regret.

LOFA achieves higher instantaneous rewards.

Experimental results on real social networks validate its effectiveness.

Abstract

We study the problem of influence maximization (IM) in an online setting, where the goal is to select a subset of nodes$\unicode{x2014}$called the seed set$\unicode{x2014}$at each time step over a fixed time horizon, subject to a cardinality budget constraint, to maximize the expected cumulative influence. We operate under a full-bandit feedback model, where only the influence of the chosen seed set at each time step is observed, with no additional structural information about the network or diffusion process. It is well-established that the influence function is submodular, and existing algorithms exploit this property to achieve low regret. In this work, we leverage this property further and propose the Lazy Online Forward Algorithm (LOFA), which achieves a lower empirical regret. We conduct experiments on a real-world social network to demonstrate that LOFA achieves superior performance compared to existing bandit algorithms in terms of cumulative regret and instantaneous reward.