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This paper aims at comparing two coupling approaches as basic layers for building clustering criteria, suited for modularizing and clustering very large networks. We briefly use optimal transport theory as a starting point, and a way as well, to derive two canonical couplings: statistical independence and logical indetermination. A symmetric list of properties is provided and notably the so called Monges properties, applied to contingency matrices, and justifying the $otimes$ versus $oplus$ notation. A study is proposed, highlighting logical indetermination, because it is, by far, lesser known. Eventually we estimate the average difference between both couplings as the key explanation of their usually close results in network clustering.
In this note, we prove a tight lower bound on the joint entropy of $n$ unbiased Bernoulli random variables which are $n/2$-wise independent. For general $k$-wise independence, we give new lower bounds by adapting Navon and Samorodnitskys Fourier proo
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Network models with latent geometry have been used successfully in many applications in network science and other disciplines, yet it is usually impossible to tell if a given real network is geometric, meaning if it is a typical element in an ensembl