Fast Change Point Detection on Dynamic Social Networks

Fast Change Point Detection on Dynamic Social Networks

Yu Wang, Aniket Chakrabarti, David Sivakoff, Srinivasan Parthasarathy

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2992-2998. https://doi.org/10.24963/ijcai.2017/417

A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.
Keywords:
Machine Learning: Data Mining
Machine Learning: Time-series/Data Streams