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Hi @tungdanganh @MihirDharmadhikari ,
Thank you very much for your work. I've read both your papers and the youtube video detailing the entire exploration algorithm. When I try your released code, in…
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Observing that this repo only uses Leidan, it would not be hard to propose the use of other Community Detection algorithms. One library comes to mind https://cdlib.readthedocs.io/
And for Bipartite c…
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The Louvain clustering algorithm is a well-established algorithm for finding communities in a graph network. It is a central part of the algorithm used by the RNASeq scientists for discovering cell ty…
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Observing https://github.com/jboynyc/textnets/blob/trunk/docs/tutorial.rst it uses the Degree-Closeness-Betweenness Trifecta for explaining the term and topic significance.
For example, for Network…
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This is not a new concept.
#### Refs:
- [FiRE ](https://www.nature.com/articles/s41467-018-07234-6): doesn't even cluster cells. Instead, uses "sketching" technique to bin cells with similar prof…
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Conventional graph clustering algorithms (e.g. spectral clustering or k-means) only considers edge weights and doesn't take vertex weights into account. In other words, they are used to find "minimum …
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## 🐛 Bug
I would like to pre-train the FairSeq Roberta base model using torch-xla on GPU. I am following the FairSeq [Pretraining RoBERTa using your own data tutorial](https://github.com/pytorch/…
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Truly, huge graphs (1,000,000s of nodes) may not produce useful visualizations with all nodes being plotted. In these cases, we may want an option to contract nodes into their clusters based on availa…
hcars updated
3 years ago
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Hi Jean and team,
Thanks for making this cool work available! I am working through your [spatially-informed clustering tutorial](https://github.com/JEFworks-Lab/MERINGUE/blob/master/docs/spatial_cl…
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The classical K-Means clustering has been also widely used in many papers, including graph-based repreentation learning. Could you consider implementing it as a new feature?