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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?

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  • $\begingroup$ 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. $\endgroup$ – Emre Oct 17 '17 at 1:26
  • $\begingroup$ @Emre is it essential to have edges in between nodes (i.e, my word2vec word vectors) to use community detection algorithms? $\endgroup$ – Volka Oct 17 '17 at 1:30
  • $\begingroup$ You can't have a network or graph without edges, but your algorithm might be defining them implicitly, like I suggested. $\endgroup$ – Emre Oct 17 '17 at 1:40
  • $\begingroup$ Thanks a lot. I never thought about it. Btw do you have any suggestions to implicitly define these threshold values? $\endgroup$ – Volka Oct 17 '17 at 2:04
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    $\begingroup$ No, but I'd take a step back to ask what your ultimate goal is and whether this is the best approach. $\endgroup$ – Emre Oct 17 '17 at 2:27
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"Improving Community Detection in in Wikipedia Articles using Semantic Features" This paper talks about various methods of community detection and might be helpful.

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