Abstract
A cycle in a graph is a subset of a connected component with redundant additional connections. If there are many cycles in a connected component, the connected component is more densely connected. While the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, enumerating cycles in the network is not easy and often requires brute force enumerations. In this study, we present a new scalable algorithm for enumerating the number of cycles in the network. We show that the number of cycles is monotonically decreasing with respect to the filtration values during graph filtration. We further develop a new statistical inference framework for determining the significance of the number of cycles. The methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the functional human brain network.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 113-116 |
| Number of pages | 4 |
| Journal | Proceedings. IEEE International Symposium on Biomedical Imaging |
| Volume | 2019-April |
| DOIs | |
| State | Published - Nov 6 2019 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2021-09-09Acknowledgements: We thank Martin Lindquist of Johns Hopkins University, Hernando Ombao of King Abdullah University of Science and Technology, Gregory Kirk of University of Wisconsin-Madison and Alex DiChristofano of Washington University at St. Louise for supports and discussions
This publication acknowledges KAUST support, but has no KAUST affiliated authors.