Not-so-good clustering in experiments about verse

[xgfs]

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.

[hadisfr]

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?

[xgfs]

Did you calculate the modularity of Louvain algorithm and of, say, k-means on the embedding? Are they comparable?

[hadisfr]

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?

[hadisfr]

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!