Comments (3)
Hello,
Thank you for your interest in our package!
To answer your questions:
-
Such algorithms would indeed call for a new submodule, whose name can be chosen upon your pull request. What outputs do you think those algorithms should offer?
-
The guidelines for contributing can be found in the Wiki. For SciPy's
LinearOperator
, we relied on the documentation. -
We do not use
scipy.linalg.pinv
as we found it was too costly. However, if you are interested in implementing any papers in particular, please do tell us so we, in turn, can tell you if we think it would make a nice addition to the package.
Looking forward to your answer!
from scikit-network.
Hi,
We took a closer look at the algorithms you proposed.
-
If you want to get your hands on the package, the easiest algorithm to implement is probably Diffusion maps. This would go directly into
sknetwork.embedding
so you can rely on examples such assknetwork.embedding.Spectral
. Most of the necessary primitives seem to be already available insknetwork.linalg
.
Do not hesitate to open a dedicated issue or directly a PR so that we can help you! -
Network Distances could go into
sknetwork.ranking
. I would need to have a closer look at the algorithm though to check whether you need Cython or not for this one. -
Edge filtering would require to implement a new submodule from scratch, something like
sknetwork.edge_filtering
. So, this is slightly more challenging. Maybe we could wait until you are more familiar with the package (and we are more familiar with the algorithm...) for this one. Still, it is an interesting project!
Thanks !
from scikit-network.
Definitely the papers I've linked! Actually, the author has implementations in pandas+nx to start from, though they are essentially single scripts.
see:
Both of those would make excellent submodules, IMO. While I am familiar with the algorithms and can use those scripts as a launching point, it would honestly be a little while until I adequately understand your framework to meaningfully contribute. If you decide to add them first, however, I could certainly contribute tests, or add remaining algorithms once I have one or two to follow from.
from scikit-network.
Related Issues (20)
- Feature Request - Graph Isomorphism HOT 1
- Confusing results from shuffle_nodes in Louvain algorithm HOT 4
- Cannot load package HOT 4
- data.load_edge_list is missing in 0.26.0 version HOT 1
- Single commodity flow feature HOT 1
- umpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObjec HOT 5
- python -m pip install scikit-network/ fails because it is looking for /triangles.cpp: HOT 1
- Installing the latest version HOT 2
- Verbose argument not expected in regression.Dirichlet fit_transform method though in description HOT 4
- Original node name mappings HOT 1
- PageRank-based recommendations HOT 1
- module 'sknetwork.data' has no attribute 'from_edge_list' HOT 5
- Error when cutting Paris dendrogram HOT 6
- KMeans does not work with numpy.ndarray HOT 1
- Feature Request GNN for KNN HOT 7
- Error installing on Linux for Python 3.11 HOT 2
- cython: linetrace=True causes TypeError: 'NoneType' object is not callable error HOT 8
- from sknetwork.utils import edgelist2adjacency, edgelist2biadjacency HOT 1
- do you have some where description for bimodularity HOT 5
- From Louvain to Leiden: guaranteeing well-connected communities. HOT 2
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