IRIE: Scalable and Robust Influence Maximization in Social Networks

TL;DR

IRIE is a scalable, robust influence maximization algorithm that efficiently handles large social networks by combining message passing influence ranking and estimation, outperforming existing methods in speed and stability.

Contribution

The paper introduces IRIE, a novel influence maximization algorithm that integrates message passing techniques for influence ranking and estimation, offering superior scalability and robustness.

Findings

IRIE matches influence coverage of existing algorithms.

IRIE is up to 100 times faster than state-of-the-art methods.

IRIE uses significantly less memory than competitors.

Abstract

Influence maximization is the problem of selecting top $k$ seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates a new message passing based influence ranking (IR), and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we demonstrate that IRIE matches the influence coverage of other algorithms while scales much better than all other algorithms. Moreover IRIE is more robust and stable than other algorithms both in running time and memory usage for various density of networks and cascade size. It runs up to two orders of magnitude faster than other state-of-the-art algorithms such as PMIA for large networks with tens of millions of nodes and edges, while using only a fraction of memory comparing with PMIA.