Maximizing Influence Propagation in Networks with Community Structure

TL;DR

This paper investigates influence propagation in networks with community structures, revealing that traditional strategies may be suboptimal in critical systems and proposing community-aware modifications for better influence maximization.

Contribution

It introduces a study of influence models with critical behavior, highlighting the importance of community structure and proposing improved targeting strategies.

Findings

Community structure significantly affects influence spread.

Simple strategies may be suboptimal in critical influence models.

Modified strategies considering communities improve influence maximization.

Abstract

We consider the algorithmic problem of selecting a set of target nodes that cause the biggest activation cascade in a network. In case when the activation process obeys the diminishing returns property, a simple hill-climbing selection mechanism has been shown to achieve a provably good performance. Here we study models of influence propagation that exhibit critical behavior, and where the property of diminishing returns does not hold. We demonstrate that in such systems, the structural properties of networks can play a significant role. We focus on networks with two loosely coupled communities, and show that the double-critical behavior of activation spreading in such systems has significant implications for the targeting strategies. In particular, we show that simple strategies that work well for homogeneous networks can be overly sub-optimal, and suggest simple modification for improving the performance, by taking into account the community structure.