Influence Maximization in Continuous Time Diffusion Networks

TL;DR

This paper introduces a new approach for influence maximization in continuous time diffusion networks, using Markov chains to analytically compute influence spread and proposing an efficient approximation algorithm with strong performance guarantees.

Contribution

The paper develops a novel analytical framework using continuous time Markov chains and presents a near-optimal approximation algorithm for influence maximization in continuous time networks.

Findings

Our algorithm outperforms existing methods by at least 20% on synthetic and real networks.

The influence maximization problem in continuous time is NP-hard.

The proposed method is robust across different network topologies.

Abstract

The problem of finding the optimal set of source nodes in a diffusion network that maximizes the spread of information, influence, and diseases in a limited amount of time depends dramatically on the underlying temporal dynamics of the network. However, this still remains largely unexplored to date. To this end, given a network and its temporal dynamics, we first describe how continuous time Markov chains allow us to analytically compute the average total number of nodes reached by a diffusion process starting in a set of source nodes. We then show that selecting the set of most influential source nodes in the continuous time influence maximization problem is NP-hard and develop an efficient approximation algorithm with provable near-optimal performance. Experiments on synthetic and real diffusion networks show that our algorithm outperforms other state of the art algorithms by at least ~20% and is robust across different network topologies.