Open Problem: Learning Dynamic Network Models from a Static Snapshot

Jan Ramon, Constantin Comendant
Proceedings of the 25th Annual Conference on Learning Theory, PMLR 23:45.1-45.3, 2012.

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 this Paper

BibTeX
@InProceedings{pmlr-v23-ramon12, 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}, pages = {45.1--45.3}, year = {2012}, editor = {Mannor, Shie and Srebro, Nathan and Williamson, Robert C.}, volume = {23}, series = {Proceedings of Machine Learning Research}, address = {Edinburgh, Scotland}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v23/ramon12/ramon12.pdf}, url = {https://proceedings.mlr.press/v23/ramon12.html}, 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.} }
Endnote
%0 Conference Paper %T Open Problem: Learning Dynamic Network Models from a Static Snapshot %A Jan Ramon %A Constantin Comendant %B Proceedings of the 25th Annual Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2012 %E Shie Mannor %E Nathan Srebro %E Robert C. Williamson %F pmlr-v23-ramon12 %I PMLR %P 45.1--45.3 %U https://proceedings.mlr.press/v23/ramon12.html %V 23 %X 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.
RIS
TY - CPAPER TI - Open Problem: Learning Dynamic Network Models from a Static Snapshot AU - Jan Ramon AU - Constantin Comendant BT - Proceedings of the 25th Annual Conference on Learning Theory DA - 2012/06/16 ED - Shie Mannor ED - Nathan Srebro ED - Robert C. Williamson ID - pmlr-v23-ramon12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 23 SP - 45.1 EP - 45.3 L1 - http://proceedings.mlr.press/v23/ramon12/ramon12.pdf UR - https://proceedings.mlr.press/v23/ramon12.html AB - 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. ER -
APA
Ramon, J. & Comendant, C.. (2012). Open Problem: Learning Dynamic Network Models from a Static Snapshot. Proceedings of the 25th Annual Conference on Learning Theory, in Proceedings of Machine Learning Research 23:45.1-45.3 Available from https://proceedings.mlr.press/v23/ramon12.html.

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