Influence Maximization in Continuous Time Diffusion Networks

By Manuel Gomez Rodriguez, Bernhard Schölkopf. 2012

100 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Structure and dynamics of information pathways in online media (2012, methodology): “We model diffusion processes as discrete networks of fully continuous temporal processes occurring at different rates building on our previous work [9, 11].”
Influence Maximization on Social Graphs: A Survey (2018, methodology): “Since the inﬂuence function under CT is still monotone and submodular, [48] uses the greedy framework with the lazy forward optimization for IM under CT.”