Online Influence Maximization with Local Observations

TL;DR

This paper introduces an online influence maximization approach using local node observations in large random graphs, with a bandit algorithm that guarantees effective influence spread without depending on total network size.

Contribution

It demonstrates that local observations can suffice for global influence maximization and proposes a bandit algorithm with strong theoretical guarantees in large-scale networks.

Findings

Effective influence maximization using local feedback in stochastic block and Chung--Lu models

Performance guarantees independent of total network size

Suitable for large-scale influence maximization applications

Abstract

We consider an online influence maximization problem in which a decision maker selects a node among a large number of possibilities and places a piece of information at the node. The node transmits the information to some others that are in the same connected component in a random graph. The goal of the decision maker is to reach as many nodes as possible, with the added complication that feedback is only available about the degree of the selected node. Our main result shows that such local observations can be sufficient for maximizing global influence in two broadly studied families of random graph models: stochastic block models and Chung--Lu models. With this insight, we propose a bandit algorithm that aims at maximizing local (and thus global) influence, and provide its theoretical analysis in both the subcritical and supercritical regimes of both considered models. Notably, our performance guarantees show no explicit dependence on the total number of nodes in the network, making our approach well-suited for large-scale applications.