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Parameters for Les Miserables dataset #2
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Please direct all questions regarding the paper to adityag@cs.stanford.edu. Feel free to open an issue if there is any clarification specific to the node2vec implementation provided in this repository. |
Sure ¯_(ツ)_/¯ |
@mewwts did you get the answer? |
Hey @Tixierae - I got some parameters from @aditya-grover back then. For word2vec |
@mewwts many thanks for the quick reply! So they did use a non-default window size (the default is 10). It seems indeed to be a critical tuning parameter that really depends on the graph (e.g. see Figure 2 of Watch your step: Learning graph embeddings through attention - from Google). |
Exactly, @Tixierae! Thanks for linking to that paper, looks like a good read. Printed it now. I was not able to find the values of those parameters sadly. The email I got from @aditya-grover said the random-walk parameters were set to "very low values" due to network size being small. |
thanks @mewwts ! |
@Tixierae I think the best thing you can do for now is try to grid search these parameters. The network is quite small right? |
@mewwts yes, each network is small, but I have thousands of them, for several datasets. The final task is graph classification, for which I am 10-fold cross validating a 2D CNN, with many epochs for each fold (I'm using this approach). So, I can do a coarse grid search, but each combination of parameters is quite costly to test. Hence, getting good priors would help a lot. |
@mewwts section 8 of this paper: http://projekter.aau.dk/projekter/files/259997796/mi109f17___Vertex_Similarity.pdf |
Thanks @Tixierae - interesting! |
I was having a hard time replicating the homophily result (structural equivalence was somehow easier to replicate, idk why), thanks to this study, i was finally able to go from this: to: I guess when the graph is so small, we need to repeat the walk many times to make word2vec actually learn something; and since the window size so small, we need to walk a long way the get the surrounding community structure. And, the window size is definitely important. |
Hi, Thanks. |
Edited on 24-03-2021: I found the node2vec bin worked better than open source implementation (stellargraph in this case) I stumbled upon my notes of replicating the results today, so I modified this comment. I was frustrated by the amount of effort to replicate the result to be honest that was why I didn't document well my process. But I think that's more like a problem of node2vec itself, that the hyperparameters are really sensitive and really depends on your graph. |
Hi,
Thanks for node2vec - such an interesting idea.
Could I ask you to specify some additional parameters for the case study 4.1 in you paper so that I can reproduce the community-result?
For the top example you set
p=1
,q=0.5
, but I'm wondering what you specifiednum_walks
,walk_length
for the random walk generation, as well assize
,window
,min_count
,sg
anditer
for Word2Vec.Hope this isn't too cumbersome to reply to. Thanks again!
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