Release Notes: GraSPy 0.0.2¶
We're happy to announce the release of GraSPy 0.0.2! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as social networks and brain networks.
This release is the result of 3 months of work with over 16 pull requests by 5 contributors. Highlights include:
- Nonparametric hypothesis testing method for testing two non-vertex matched graphs.
- Plotting updates to
- Sampling degree-correlcted stochatic block models (DC-SBM).
import_edgelistfunction for importing single or multiple edgelists.
Blackformatting for the package.
- Embedding methods are now fully sklearn-compliant. This is tested via
check_estimatorfunction in sklearn.
heatmapcan now plot hierchical labels.
- New Laplacian computing method ('R-DAD') by adding a constant to the diagonal degree matrix.
- Semiparametric testing only checks for largest connected component (LCC) in the intial embeddings.
- Various bug fixes.
- Various tutorial latex fixes.
- Various documentation clarifications.
- More consistent documentation.
OmnibusEmbedclasses, which checks if input graph(s) are fully connected when
heatmapnow have a
outer_hier_labels, which are used for hierarchical labeling of nodes.
to_laplacefunction now has
regularizerarg for when
sbmfunction now has
dc_kwsarguments for sampling SBM with degree-correction.