Mixture of Mutually Exciting Processes for Viral Diffusion

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Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, background): “A sequence of recent work has argued that modeling cascade data and information diffusion using continuous-time diffusion networks can provide significantly more accurate models than discretetime models [8, 9, 10, 11, 12, 13, 14, 15].”
HAWKES PROCESSES ON LARGE NETWORKS (2016, background): “…see Bacry-Muzy [4, 5]), ﬁnancial price modelling across scales (Bacry et al. [3], Jaisson-Rosenbaum [24]), social networks interactions (Blundell et al. [7], Simma-Jordan [43], Zhou et al. [46]) and epidemiology like for instance viral diﬀusion on a network (Hang-Zha [45]), to name but a few.”
Dynamics of information diffusion and its applications on complex networks (2016, methodology): “The third type is based on the probabilistic model, for example, using the theory of exciting point processes (Hawkes process) [94,185].”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, methodology): “, [22, 23, 33, 34, 36]) is that we assume the process intensity λt depends on another stochastic process pt, the post infectiousness.”
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (2017, methodology): “Temporal point processes have been shown as very effective tool to model various intricate temporal behaviors in networks (Yang & Zha, 2013; Farajtabar et al., 2014; 2015; Du et al., 2015; 2016; Wang et al., 2016a;b;c; 2017a;b).”