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Reiterate the process until a component of size (ge c/alpha ) is generated or, otherwise, a profile consisting of isolated strong components smaller than (c/alpha ) is generated.
Our first observation is that the dictionaries learned by KSVD and ITKrM on clean data with S=5 and after removing a low-rank component of size L=3 or L=7 perform best, indicating the importance of removing the low-rank component to get a well-conditioned dictionary.
Algorithmic setup: Via ITKrMM, we first learn the low-rank component of size L=1,3,7, and a dictionary of size K=2d−L, resulting in a system with redundancy 2. We set the sparsity level in the dictionary learning to S=8−L for L=1,3 corresponding to an overall sparsity L+S=8 and to S=5 for L=7, corresponding to an overall sparsity L+S=12.
More importantly, attempting to extract PDSs from this results in one super component of size 350, and 3 components of sizes 4, 4 and 6.
For the large connected component of size 3429, we ran a graph-clustering method called spectral clustering [ 44] to further divide it into eight smaller subnetworks.
In general, for any connected component of size k>3 with at least than k k−1)/4 edges, more edges will be added with score NC>0.5, yielding a denser component and increasing network transitivity overall.
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The largest part of the network consists of one large connected component of 3.4 million users, the rest are components of size smaller than 1000 users.
Learning setup: The dictionary learning setup is the same as in the experiments for 30 and 50% corruption levels in Section 5.2.1, where for ITKrMM, we consider low-rank components of size L=1 and L=3, abbreviated as ITKrMM1 and ITKrMM3 respectively.
There are also 19 smaller connected components of size ranging from 2 to 15.
First,∼10% of components of size 2 break up into singletons in this range.
(I eliminate isolated reactions, that is, components of size one from this graph).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com