Influence Maximization in Continuous Time Diffusion Networks

132 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, methodology): “We adopt the definition of influence as the average number of infected nodes given a set of source nodes and a time window, as in previous work [7].”
Influence Maximization in Near-Linear Time: A Martingale Approach (2015, methodology): “For each node v , we sample a triggering set of v from T ( v ) , and remove any incoming edge of v that starts from a node not in the triggering set.”
Influence Maximization on Social Graphs: A Survey (2018, methodology): “As the CT model can be described as a continuous time Markov chain (CTMC) [48], exactly estimating the inﬂuence spread under CT is initially solved as an inference problem for graphical models, which is also #P-hard.”
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].”