node2vec uses CBOW instead of skip-gram about nodevectors HOT 4 OPEN

Thanks, can you confirm empirically that 1 is better than 0?

If so, I'll change the default along with other udpates this week.

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It is for my graphs, but I'm not sure if this is always the case. There is some discussion about which one is better, but in the context of NLP. I couldn't find anyone discussing that in the context of graph embeddings.

Anyway, I suggest you to not only use 1 as the default, but also force w2vparams["sg"] to 1 instead of leaving this decision to the programmer, or make this a separate parameter in the same way you did with w2vparams["workers"]. The reason is that it is very easy for the programmer to forget to set this parameter when he wants to customize other w2v parameters (this was exactly how I stumbled upon this). And node2vec was built explicitly with skip-gram in mind.

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Check the reference below for skip-gram vs CBOW. The quality difference between the two seems to be the fault of a longstanding implementation error in the original word2vec and Gensim implementations.

İrsoy, Ozan, Adrian Benton, and Karl Stratos. “Koan: A Corrected CBOW Implementation.” ArXiv:2012.15332 [Cs, Stat], December 30, 2020. http://arxiv.org/abs/2012.15332.

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Just passing through, noticed this issue in the course of answering someone's Node2Vec/Word2Vec interaction question, thought I'd mention:

• In the decade since word2vec arrived on the scene, I've not seen a strong consensus emerge on whether SG or CBOW is generally better. There are some reasons to suspect CBOW's batching of more operations allows more data/training in the same amount of time... but for some datasets/purposes/parameters/evaluations, SG still does better. So while you might want to default to SG if the original specification of 'Node2Vec' implied it creating a user expectation for that mode, perhaps, but otherwise there may not be a strong reason to nudge users any particular way - leaving it up to the advanced optimizers to dig deeper.
• I'm not convinced that the 'Koan' paper authors have identified anything 'incorrect' about usual word2vec implementations (including the original word2vec code, & later Facebook FastText code, by some of the original word2vec paper authors). Their paper shows a mix of both better & worse evaluations after their change (depending on the evaluation – & some of the claimed improvements might be due to other changes in the performance or behavior of their code, besides their reinterp of CBOW backpropagation. In particular, tinkering with Gensim Word2Vec parameters cbow_mean, alpha, & (new since their paper) shrink_windows might match or exceed any benefits they've observed (on similar data/evals), without fully adopting their CBOW interpretation. See my longer comments in a Gensim issue for more details.

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