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By confirming that social partner selection is strongly driven by structural, geographical, and topical proximity, we develop a machine-learning social link recommender for individual users trained on a set of features selected as best predictive out of several and we test it on the still widely unexplored domain of a network of interest.
Finally, combining the project names or Lead Investigators for the GMTF and the MPowerment Project (because of topical proximity), for the GMHT and the 4 Gyms Project (because of the shared UK context) and for the GMTF and the ASSIST Project (because of their UK context and use of Diffusion of Innovations Theory) also yielded interesting insights.
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Phrase frequency analysis (the occurrence frequency of multiword technical phrases) provides the pervasive technical themes of the topical databases of interest, and phrase proximity analysis provides the relationships among the pervasive technical themes.
We have found that link formation has a strong propensity to topical and structural selection effects, reciprocity, and proximity-driven attachment.
Topical, huh?
Topical thriller?
Mmm, topical.
Proximity is also key.
A gripping, topical adventure.
Mary Tyler Moore is topical.
Even Dean Martin is topical.
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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