How to set parameters for a collection small disconnected graphs about node2vec HOT 2 CLOSED

@eliorc Thank you for the reply. I had done all that you describe and read several papers before asking the question. However, everything I've read seems to assume one large mostly connected graph esp. a social network or citations. I see my case as substantively different. I'll keep looking. Thanks again for your time.

from node2vec.

It's hard to say, but I'll try my best to explain the parameters' impact on your result.
Essentially you decide how you want to embed information from your graph into the embedding space using the different hyperparameters.
I'd say num_walk is mostly for robustness, if your graphs are small enough I don't think you will gain from greater num_walk - this only defines the number of walks you will created from each node. In turn, these random walks are the input data for a skip-gram algorithm (just like Word2Vec). So you are just creating more data.

Walk length, jointly with window_size (which if you don't set, is set to 5 by gensim), q and p define how these walks are constructed. It will be too much for me to explain here, but if you want to understand this better I'd suggest to

1. Learn about the Word2Vec algorithm, and the role of window size in the algorithm
2. Learn about q and p (you can do so from looking at my PyData video in the README)

I think this will give you enough context to understand what you are looking for

from node2vec.