New blog post: "Descriptive vs. inferential community detection"

A mini-thread for those too lazy to click the link! 1/7

(Based on recent pre-print:

There are essentially two objectives when doing community detection (or any data analysis): To "describe" or to "infer".

Description involves finding patterns, inference involves finding explanations. 2/7

Most community detection methods out there (modularity, infomap, walktrap, etc.) are descriptive. The communities they find are there, but they cannot explain. In a very concrete sense, they *overfit* your data, confusing actual structure with random fluctuations. 3/7

Inferential approaches do not do this. They find the most parsimonious explanation for the data — according to Occam's razor — and do not confuse randomness with structure. 4/7

So, when should we "infer" or "describe"? Here's a good litmus test:

Q: "Would the usefulness of our conclusions change if we learn, after obtaining the communities, that the network being analyzed is completely random?"


If the answer is "yes", then an inferential approach is needed.

If the answer is "no", then an inferential approach is not required. 6/7


It's arguable that in most scientific contexts the answer would be "yes". I this case we need inferential approaches, and descriptive ones are just not up to the task!

There is lots more to say about this. Read it here: 7/7

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