Influence Maximization in Continuous Time Diffusion Networks
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.
Cite
@article{arxiv.1205.1682,
title = {Influence Maximization in Continuous Time Diffusion Networks},
author = {Manuel Gomez Rodriguez and Bernhard Schölkopf},
journal= {arXiv preprint arXiv:1205.1682},
year = {2012}
}
Comments
To appear in the 29th International Conference on Machine Learning (ICML), 2012. Website: http://www.stanford.edu/~manuelgr/influmax/