nodevectors
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VHRanger
Fastest network node embeddings in the west
The parameter is now called `vector_size`. See: https://stackoverflow.com/a/67080756
Hello! Thanks for the great great work! I encountered an issue while using nodevectors to train the prone embeddings: I ran G = cg.read_edgelist("..", directed=True, sep=',') g2v = ProNE() g2v.fit(G)...
Hello, I just tryied to fit Node2Vec object and got error 129 # Train gensim word2vec model on random walks 130 self.model = gensim.models.Word2Vec( 131 sentences=self.walks, 132 size=self.n_components, I found...
I have trained and saved the model with ``` import csrgraph as cg import nodevectors G = cg.read_edgelist("edges.txt", directed=False, sep=' ') ggvec_model = nodevectors.GGVec() embeddings = ggvec_model.fit_transform(G) ggvec_model.save("embeddings.emb") ``` Now...
See: https://stackoverflow.com/questions/53195906/getting-init-got-an-unexpected-keyword-argument-document-this-error-in
Node2vec and DeepWalk original proposals are built upon the skip-gram model. By default, nodevectors does not set the parameter ```w2vparams["sg"]``` to 1, therefore the underlying Word2Vec model uses the default...
It appears one of the argument names has changed in the newly released version of GenSim. This has also caused some pain in other libraries using this package for node2vec...
I need the option to assign random state or seed values to get stable results. I don't think there is such an option. Unfortunately, my attempts to fix the general...
Hi!, Related to https://github.com/VHRanger/nodevectors/issues/40 I was wondering if node2vec now uses skip-gram by default (I cannot see it anywhere in the source code, but i am sure i am missing...
Hi, Can I update node embeddings given and already trained model? I want to fit a model but then I want to update the network periodically and update the node...