Geometric Network Comparisons

Dena Asta Carnegie Mellon University, Cosma Shalizi Carnegie Mellon University
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:762-770, 2015.

Abstract

Network analysis has a crucial need for tools to compare networks and assess the significance of differences between networks. We propose a principled statistical approach to network comparison that approximates networks as probability distributions on negatively curved manifolds. We outline the theory, as well as implement the approach on simulated networks.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-university15q, title = {Geometric Network Comparisons}, author = {University, Dena Asta Carnegie Mellon and University, Cosma Shalizi Carnegie Mellon}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {762--770}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/university15q/university15q.pdf}, url = {https://proceedings.mlr.press/r13/university15q.html}, abstract = {Network analysis has a crucial need for tools to compare networks and assess the significance of differences between networks. We propose a principled statistical approach to network comparison that approximates networks as probability distributions on negatively curved manifolds. We outline the theory, as well as implement the approach on simulated networks.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Geometric Network Comparisons %A Dena Asta Carnegie Mellon University %A Cosma Shalizi Carnegie Mellon University %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-university15q %I PMLR %P 762--770 %U https://proceedings.mlr.press/r13/university15q.html %V R13 %X Network analysis has a crucial need for tools to compare networks and assess the significance of differences between networks. We propose a principled statistical approach to network comparison that approximates networks as probability distributions on negatively curved manifolds. We outline the theory, as well as implement the approach on simulated networks. %Z Reissued by PMLR on 04 October 2026.
APA
University, D.A.C.M. & University, C.S.C.M.. (2015). Geometric Network Comparisons. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:762-770 Available from https://proceedings.mlr.press/r13/university15q.html. Reissued by PMLR on 04 October 2026.

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