Discovering Long Range Properties of Social Networks with Multi-Valued Time-Inhomogeneous Models
Abstract
The current methods used to mine and analyze temporal social network data make two assumptions: all edges have the same strength, and all parameters are time-homogeneous. We show that those assumptions may not hold for social networks and propose an alternative model with two novel aspects: (1) the modeling of edges as multi-valued variables that can change in intensity, and (2) the use of a curved exponential family framework to capture time-inhomogeneous properties while retaining a parsimonious and interpretable model. We show that our model outperforms traditional models on two real-world social network data sets.
Cite
Text
Wyatt et al. "Discovering Long Range Properties of Social Networks with Multi-Valued Time-Inhomogeneous Models." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7666Markdown
[Wyatt et al. "Discovering Long Range Properties of Social Networks with Multi-Valued Time-Inhomogeneous Models." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/wyatt2010aaai-discovering/) doi:10.1609/AAAI.V24I1.7666BibTeX
@inproceedings{wyatt2010aaai-discovering,
title = {{Discovering Long Range Properties of Social Networks with Multi-Valued Time-Inhomogeneous Models}},
author = {Wyatt, Danny and Choudhury, Tanzeem and Bilmes, Jeff A.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2010},
pages = {630-636},
doi = {10.1609/AAAI.V24I1.7666},
url = {https://mlanthology.org/aaai/2010/wyatt2010aaai-discovering/}
}