Uncover Topic-Sensitive Information Diffusion Networks

100 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): “…synthetic and real-world data show that the proposed algorithm can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.”
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015, background): “Information diffusion [8, 12] also relies on user activity to describe influence, but focuses on understanding the widespread flow of information.”
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].”
Time-Sensitive Recommendation From Recurrent User Activities (2015, methodology): “For instance, we can make the base intensity Λ0 depend on user-profiles and item-contents [9, 7].”