Open Problem: Learning Dynamic Network Models from a Static Snapshot
Abstract
In this paper we consider the problem of learning a graph generating process given the evolving graph at a single point in time. Given a graph of sufficient size, can we learn the (repeatable) process that generated it? We formalize the generic problem and then consider two simple instances which are variations on the well-know graph generation models by Erdós-Rényi and Albert-Barabasi.
Cite
Text
Ramon and Comendant. "Open Problem: Learning Dynamic Network Models from a Static Snapshot." Proceedings of the 25th Annual Conference on Learning Theory, 2012.Markdown
[Ramon and Comendant. "Open Problem: Learning Dynamic Network Models from a Static Snapshot." Proceedings of the 25th Annual Conference on Learning Theory, 2012.](https://mlanthology.org/colt/2012/ramon2012colt-open/)BibTeX
@inproceedings{ramon2012colt-open,
title = {{Open Problem: Learning Dynamic Network Models from a Static Snapshot}},
author = {Ramon, Jan and Comendant, Constantin},
booktitle = {Proceedings of the 25th Annual Conference on Learning Theory},
year = {2012},
pages = {45.1-45.3},
volume = {23},
url = {https://mlanthology.org/colt/2012/ramon2012colt-open/}
}