Comments (1)
palash1992
commented on May 24, 2024
Hi,
For big data sets, it takes days to train a model. Yes, the experimental setup is indeed powerful as it takes a lot longer on smaller computers. Since most models have many hyperparameters to tune, a good computer is needed to find optimal configuration for a model.
If you have a smaller computer, I would recommend using GF and node2vec as they are the fastest among the models and give pretty good results.
Regards,
Palash Goyal
from gem.
Related Issues (20)
- Error in SDNE
- Tensorflow module is missing
- sdne: Nodes corresponding to embedded vectors HOT 1
- Problem with dependencies HOT 3
- LaplacianEigenmap for a large number of connected components HOT 3
- result of the running example of readme.md HOT 2
- The use of "merge" module of keras.layers in sdne.py HOT 2
- EXECUTION HOT 3
- SDNE execution problem HOT 1
- how to use GEM in an exsisting graph HOT 4
- gf not found HOT 2
- Use of Link prediction code HOT 2
- The number of positive classes for each node is leaked to TopKRanker HOT 1
- How to determine dimension variable in methods HOT 1
- Create embeddings directly from an adjacency matrix (e.g. numpy.array or scipy.sparse)? HOT 2
- [Errno 2] No such file or directory: 'gem/intermediate/karate_gf.graph' HOT 7
- SDNE implementation error HOT 3
- Error when running link prediction
- Unweighted node2vec not possible?
- Error running SDNE algorithm HOT 1
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