Scalable Influence Estimation in Continuous-Time Diffusion Networks

TL;DR

This paper introduces a scalable randomized algorithm for influence estimation in continuous-time diffusion networks, enabling efficient influence maximization in large-scale networks with high accuracy.

Contribution

The paper presents a novel randomized influence estimation method that scales to millions of nodes and provides theoretical guarantees for influence maximization.

Findings

Algorithm scales to networks of millions of nodes

Significantly improves influence estimation accuracy

Achieves near-optimal influence maximization results

Abstract

If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages? This influence estimation problem is very challenging since both the time-sensitive nature of the problem and the issue of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuous-time diffusion networks. Our algorithm can estimate the influence of every node in a network with |V| nodes and |E| edges to an accuracy of $\varepsilon$ using $n=O(1/\varepsilon^2)$ randomizations and up to logarithmic factors O(n|E|+n|V|) computations. When used as a subroutine in a greedy influence maximization algorithm, our proposed method is guaranteed to find a set of nodes with an influence of at least (1-1/e)OPT-2$\varepsilon$, where OPT is the optimal value. Experiments on both synthetic and real-world data show that the proposed method can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.