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As I understand we can apply community detection algorithms such as Louvain to detect communities in a social network (i.e. involves people).

But I am quite interested in knowing if we can use the same community detection algorithms such as Louvain to identify communities in word vector space (e.g., word2vec), instead of clustering?

How are you defining the edges, by thresholding a distance? I suppose you could, as long as your embeddings are not too high dimensional; just try it. – Emre – 2017-10-17T01:26:28.323

@Emre is it essential to have edges in between nodes (i.e, my word2vec word vectors) to use community detection algorithms? – Volka – 2017-10-17T01:30:20.587

You can't have a network or graph without edges, but your algorithm might be defining them implicitly, like I suggested. – Emre – 2017-10-17T01:40:36.567

Thanks a lot. I never thought about it. Btw do you have any suggestions to implicitly define these threshold values? – Volka – 2017-10-17T02:04:53.600

1No, but I'd take a step back to ask what your ultimate goal is and whether this is the best approach. – Emre – 2017-10-17T02:27:09.450

@Emre Thanks. I actually want to cluster my vectors. I am currently using k-means. But it does not seem to work well. That is why I am looking for other possibilities of clustering such as community detection algorithms. – Volka – 2017-10-17T04:41:42.503