Fast Change Point Detection on Dynamic Social Networks
Abstract
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.
Cite
Text
Wang et al. "Fast Change Point Detection on Dynamic Social Networks." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/417Markdown
[Wang et al. "Fast Change Point Detection on Dynamic Social Networks." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/wang2017ijcai-fast/) doi:10.24963/IJCAI.2017/417BibTeX
@inproceedings{wang2017ijcai-fast,
title = {{Fast Change Point Detection on Dynamic Social Networks}},
author = {Wang, Yu and Chakrabarti, Aniket and Sivakoff, David and Parthasarathy, Srinivasan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {2992-2998},
doi = {10.24963/IJCAI.2017/417},
url = {https://mlanthology.org/ijcai/2017/wang2017ijcai-fast/}
}