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Comparison between weighted and unweighted hypergraphs in clustering #99
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We have contacted the authors and will look with them at your code. Will post when we have more information. |
I have run into the same issue. If I run:
If I try the line that young917 suggested above, it doesn't work either. |
This is a bug. We are working on it. |
@thosvarley and @young917 HNX 2.0 will be released on Saturday May 13. You will add cell weights to the incidence matrix using the cell_weights keyword in the hypergraph constructor. Please read the documentation for formatting and let us know if anything is unclear. |
Merge in HYP/hypernetx from releases/v2.0.2 to develop * commit 'dd76f358ef5d6f2b76c24260bb7edb6ad4ac98c0': bump: version 2.0.1 → 2.0.2 Fix import try catch block; update pypi workflow
Hello, thank you for sharing this excellent library.
I want to compare the performance of clustering on weighted and unweighted hypergraphs as described in K. Hayashi, S. Aksoy, C. Park, H. Park, "Hypergraph random walks, Laplacians, and clustering".
I tried to do this based on Tutorial 11.
However, I think some modifications need.
h = hnx.Hypergraph(hnx.StaticEntitySet(data=data, weights=w))
I think this code does not make a weighted hypergraph.
Instead, this should be revised as below.
h = hnx.Hypergraph(hnx.StaticEntitySet(data=data, weights=w), weights=w)
Additionally, I cannot get the satisfying result that weighted hypergraphs perform better than unweighted ones.
Thus, I want to ask whether the below code is a correct way to make clustering on an unweighted hypergraph and evaluate clustering algorithms by NMI scores.
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