Predicting information cascade by self-exciting point process model

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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.