Learning Networks of Heterogeneous Influence

By Nan Du, Le Song, Alex Smola, Ming Yuan. 2012

168 citations recorded by the source corpus.

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Citations in the corpus

Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, methodology): “Extensive experimental comparisons on both synthetic and real world data showed that continuous-time models yield significant improvement in settings such as recovering hidden diffusion network structures from cascade data [8, 10] and predicting the timings of future events [9, 14].”
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector (2016, background): “More recently, the self-excitation point process [20], has become an ongoing hot topic for modeling the latent dynamics of information diffusion [16, 9, 10, 8, 40, 14, 22], online-user engagement [13], news-feed streams [7], and context-aware recommendations [11].”
Learning Triggering Kernels for Multi-dimensional Hawkes Processes (2013, background): “The recent work of (Du et al., 2012) considers the problem of learning the triggering kernel for diffusion processes based on linear combination of basis functions.”
Modeling Information Propagation with Survival Theory (2013, methodology): “The kernelized hazard functions used in KernelCascade (Du et al., 2012) map into our model by considering m covariates per parent, where k(τl, ·) is a kernel function and τl is the l th point in am-point uniform grid over of [0, T ].”
Learning content–social influential features for influence analysis (2016, unknown): “The second is based on network structure learning [9,12,28,32], which infer diffusion network or even estimate diffusion probabilities.”
Time-Sensitive Recommendation From Recurrent User Activities (2015, methodology): “Furthermore, we can even learn the triggering kernel directly using nonparametric methods [8, 30].”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, methodology): “Such models were mostly developed for the complementary problem of network inference, where one observes a number of information cascades and tries to infer the structure of the underlying network over which the cascades propagated [8, 10, 13, 14, 15, 18, 33, 36].”