Graph Statistics in Python (GrasPy)

Simple, flexible and powerful graph analysis library grounded in statistical theories.

GrasPy offers scikit-learn compliant APIs and it provides clear feedback upon user error.

GrasPy is open-source and commercially usable software, and released under Apache-2.0 License.

Publications
  1. J. Arroyo et al. Inference for multiple heterogenous networks with a common invariant subspace. arXiv, 2019.
  2. H. Helm, J. V. Vogelstein and C. E. Priebe. Vertex Classification on Weighted Networks. arXiv, 2019.
  3. J. Chung et al. GraSPy: Graph Statistics in Python. arXiv, 2019.
  4. C. E. Priebe et al. On a 'Two Truths' Phenomenon in Spectral Graph Clustering. PNAS, 2019.
  5. A. Athreya et al. Statistical Inference on Random Dot Product Graphs: a Survey. Journal of Machine Learning Research, 2018.
  6. S. Wang et al. Joint Embedding of Graphs. arXiv, 2017.
  7. C. E. Priebe et al. Statistical Inference on Errorfully Observed Graphs. Journal of Computational and Graphical Statistics, 2015.
  8. L. Chen et al. A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes. Worm, 2015.
  9. C. E. Priebe, J. Vogelstein and D. Bock. Optimizing the quantity/quality trade-off in connectome inference. Communications in Statistics - Theory and Methods, 2013.
  10. D. E. Fishkind et al. Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown. SIAM Journal on Matrix Analysis and Applications, 2012.
  11. J. T. Vogelstein, R. J. Vogelstein and C. E. Priebe. Are mental properties supervenient on brain properties? Scientific Reports, 2011.