Modeling Information Propagation with Survival Theory
By Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf. 2013
187 citations recorded by the source corpus.
Networks provide a 'skeleton' for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Le Song
- Nan Du
- Bernhard Schölkopf
- Jure Leskovec
- Manuel Gomez-Rodriguez
- Alex Smola
- Hongyuan Zha
- Kenji Fukumizu
Citations in the corpus
- Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, background): “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].”
- Influence Maximization in Near-Linear Time: A Martingale Approach (2015, methodology): “As an application, we present a variant of IMM for the continuous-time independent cascade (CTIC) model [6, 16, 30], which is widely adopted in the machine learning literature [15, 17, 18] but not supported by TIM.”
- Neural Survival Recommender (2017, methodology): “On the other side, survival analysis has been applied to many mining problems such as modeling the dynamics of cascade [10], document clustering in a streaming fashion when coupled with Dirichlet process [8], discovering disease relations over time [5].”
- SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, methodology): “These methods have been successfully applied to study the spread of memes on the web [10, 14, 32, 33] as well as hashtags on”