seismic: Predicting information cascade by self-exciting point process...
In seismic: Predict Information Cascade by Self-Exciting Point Process
seismic R Documentation
Predicting information cascade by self-exciting point process model
Description
This package implements a self-exciting point process model for information cascades.
An information cascade occurs when many people engage in the same acts after observing
the actions of others. Typical examples are post/photo resharings on Facebook and retweets
on Twitter. The package provides functions to estimate the infectiousness of an
information cascade and predict its popularity given the observed history.
For more information, see
http://snap.stanford.edu/seismic/.
References
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity by Q. Zhao, M. Erdogdu, H. He, A. Rajaraman, J. Leskovec, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015.
seismic documentation built on May 21, 2022, 1:05 a.m.
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| seismic | R Documentation |
Predicting information cascade by self-exciting point process model
Description
This package implements a self-exciting point process model for information cascades. An information cascade occurs when many people engage in the same acts after observing the actions of others. Typical examples are post/photo resharings on Facebook and retweets on Twitter. The package provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. For more information, see http://snap.stanford.edu/seismic/.
References
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity by Q. Zhao, M. Erdogdu, H. He, A. Rajaraman, J. Leskovec, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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