Comments (2)
Thanks a lot for your comments/questions!
Regarding 1): You're right, the way we accumulate metrics doesn't correctly account for incomplete batches. This should only make a minor difference in the evaluation scores, but please feel free to submit a pull request.
Re: 2) That's correct. For multiple relation cases, you could either iterate over all possible permutations (but this quickly becomes impractical) or use a permutation-invariant clustering similarity score for evaluation such as ARI (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html) or AMI (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html).
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Thanks very much for the confirmation/comments!
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Related Issues (20)
- Questions regarding the edges that are created in the latent space
- For the type of edge in the experimental setup. HOT 1
- Request for Kuramoto dataset HOT 5
- Error in running the simulation HOT 1
- my_softmax HOT 3
- Is it possible to learn more than 2 edge-types in unsupervised manner? HOT 4
- Undirected latent graph
- Undirected latent graph
- How to reproduce some paper results
- Where can I find the code of Eq. 12 in the paper?? HOT 2
- Compatibility with Pytorch 1.xx ?
- Sport UV dataset HOT 4
- non-interaction edge type
- Error in class MLP def forward HOT 2
- Unsupervised learning HOT 2
- what does mean edge_type? HOT 1
- what does mean logits shape?
- Results of charge experiment
- /
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