Comments (5)
Yes I agree. I am currently working on an extension of the library. Perhaps once I am done with that I can make this change for both of them.
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The nodes need to be contiguous. Do you think that can be the issue in your setting?
from gem.
Hey Palash, sorry for the late reply.
Yes that was the issue. Maybe a dict-like way for accessing the embeddings could be more appropriate for such a case - such as in gensim. What do you think? :)
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@dmpierre How did you load the embeddings without using loadEmbeddings()?
from gem.
Hi :)
Just comment out line 84
in node2vec.py
:
self._X = graph_util.loadEmbedding('tempGraph.emb')
Then you can write your own kind of loadEmbedding
function that best suits your needs.
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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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