Published December 20, 2025
| Version v4.0.1
Software
Open
Testing biological network motif significance with exponential random graph models
Authors/Creators
Description
Release created for Zenodo archiving and DOI.
Notes
If you use this software, please cite:
Stivala, A. (2023). New network models facilitate analysis of biological networks. arXiv preprint arXiv:2312.06047 https://arxiv.org/abs/2312.06047
Stivala, A. & Lomi, A. (2021). Testing biological network motif significance with exponential random graph models. Applied Network Science 6:91. https://doi.org/10.1007/s41109-021-00434-y
as well as this DOI.
Files
stivalaa/bionetworks_estimations-v4.0.1.zip
Files
(17.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:93a657aa26212c72437b8e1f557c351e
|
17.1 MB | Preview Download |
Additional details
Related works
- Is supplement to
- Journal article: 10.1007/s41109-021-00434-y (DOI)
- Preprint: arXiv:2312.06047 (arXiv)
Funding
- Swiss National Science Foundation
- Advancing the applicability of exponential random graph models (ERGMs) for the analysis of social and other networks: Algorithms, implementation, and applications 200778
- Swiss National Science Foundation
- The Global Structure of Knowledge Networks: Data, Models and Empirical Results 167326
Software
- Repository URL
- https://github.com/stivalaa/bionetworks_estimations
- Programming language
- R , Shell , Python