Comments (5)
I would feed 128d embeddings personally.
Regarding 2d vs. 128d embeddings, the objective functions of UMAP or TSNE are tailored towards visualization task. VERSE is a bit different, offering similarity preservation for analysis of graphs.
from verse.
Using 128 dimensions and then using UMAP to reduce the result to x
and y
, I ended up this:
Is this a right approach? Can I make it better?
from verse.
Did you calculate the modularity of Louvain algorithm and of, say, k-means on the embedding? Are they comparable?
from verse.
No. How can I do that? Feed the final bidimensional result of embedding to sklearn
or sth?
P.S. I saw many times this approach of feeding higher dimensional embeddings of VERSE or node2vec into UMAP to get a bidimensional embedding for visualization, and it seems to work better than using e.g. VERSE to get a bidimensional embedding directly. But I don't get it. Aren't UMAP another embedding tool just liker VERSE and node2vec, only with a different approach?
from verse.
I'll test that later this way.
Different approaches to design objective functions is an important point. I did not dig too into UMAP.
Thank you!
from verse.
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from verse.