Comments (1)
The SDNE model calculates the sum of the reconstruction loss over all nodes. Since we are looping over the edges because of first order proximity preservation, we need to cancel the effect of multiple counting of nodes. Dividing by degree solves this problem.
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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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