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aditya-grover / node2vec

http://snap.stanford.edu/node2vec/
MIT License
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Is it possible to run node2vec on multi graphs ? #103

Open astrung opened 3 years ago

astrung commented 3 years ago

As explained confirmation from all node2vec papers, this algorithm is designed for 1 graph. It create specific embedding for each graph, so it is not possible for training node2vec model on a graph, then use trained model for another graphs. In other words, if i have 2 graphs G and H, i need 2 embedding models:

G_node2vec = Node2Vec(G)
G_model =  G_node2vec.fit()
H_node2vec = Node2Vec(H)
H_model =  H_node2vec.fit()

Then we have a new graph J. Because this graph share some characteristics with graph G and H, i want to use node2vec embedding model from both model G and H to get embedding on J without training a new node2vec model for graph J. I have 2 questions:

  1. In theory, can we train node2vec for multi graphs, then use embedding for another (not trained) graph? What is possible solution?

  2. I am thinking about a solution: Disjoin all training graphs into a graph then create node2vec model on union graph, then use this model to predict non-trained graph. Is it correct ?

    union_graph = disjoint_union(G, H) union_node2vec = Node2Vec(union_graph) union_model = union_node2vec.fit() embedded_J = union_model.predict(J)

If you have any other approaches please share your ideas. I have just started in graph theory and algorithms 2 months ago

A2Amir commented 2 years ago

have you found the answer? I have the same question.

astrung commented 2 years ago

@A2Amir sorry but i dropped this approach. Let wait for the others :(

A2Amir commented 2 years ago

Please share with me your approach.