Evolving Influence Maximization in Evolving Networks

TL;DR

This paper introduces a new framework for influence maximization in evolving networks, addressing the challenges of dynamic topology and influence probabilities with a bandit-based approach that learns and adapts over time.

Contribution

It proposes EIM, a novel bandit-based framework that dynamically selects seed nodes in evolving networks, incorporating three new components to handle uncertainties from network growth and influence changes.

Findings

EIM effectively adapts seed selection to network evolution.

The framework outperforms static approaches in dynamic settings.

EIM successfully learns influence patterns over time.

Abstract

Influence Maximization (IM) aims to maximize the number of people that become aware of a product by finding the `best' set of `seed' users to initiate the product advertisement. Unlike prior arts on static social networks containing fixed number of users, we undertake the first study of IM in more realistic evolving networks with temporally growing topology. The task of evolving IM ({\bfseries EIM}), however, is far more challenging over static cases in the sense that seed selection should consider its impact on future users and the probabilities that users influence one another also evolve over time. We address the challenges through $\mathbb{EIM}$, a newly proposed bandit-based framework that alternates between seed nodes selection and knowledge (i.e., nodes' growing speed and evolving influences) learning during network evolution. Remarkably, $\mathbb{EIM}$ involves three novel components to handle the uncertainties brought by evolution: