Model-Independent Online Learning for Influence Maximization

TL;DR

This paper introduces a model-agnostic, data-efficient online learning framework for influence maximization in social networks, capable of adapting to various diffusion models and learning optimal seed sets through a novel semi-bandit approach.

Contribution

It proposes a model-independent parametrization and a surrogate function for influence maximization, along with a bandit algorithm that improves regret bounds and learns influence factors efficiently.

Findings

Framework is robust to different diffusion models.

Achieves better regret bounds compared to previous methods.

Effectively learns near-optimal seed sets in experiments.

Abstract

We consider influence maximization (IM) in social networks, which is the problem of maximizing the number of users that become aware of a product by selecting a set of "seed" users to expose the product to. While prior work assumes a known model of information diffusion, we propose a novel parametrization that not only makes our framework agnostic to the underlying diffusion model, but also statistically efficient to learn from data. We give a corresponding monotone, submodular surrogate function, and show that it is a good approximation to the original IM objective. We also consider the case of a new marketer looking to exploit an existing social network, while simultaneously learning the factors governing information propagation. For this, we propose a pairwise-influence semi-bandit feedback model and develop a LinUCB-based bandit algorithm. Our model-independent analysis shows that our regret bound has a better (as compared to previous work) dependence on the size of the network. Experimental evaluation suggests that our framework is robust to the underlying diffusion model and can efficiently learn a near-optimal solution.