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ZENODO
Model . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Model . 2023
License: CC BY
Data sources: Datacite
ZENODO
Model . 2023
License: CC BY
Data sources: Datacite
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Element Wise Products and Scaling Factors

Authors: Emmerson, Parker;

Element Wise Products and Scaling Factors

Abstract

Here, we have defined the transformed matrix as ̃A(n) and the transformedvector as ̃h(n). These transformations are carried out by computing the element-wise product between the original matrix/vector and the scaling factor, whichis formed by dividing each element in the original matrix/vector by the sum ofall its elements. This scaling factor ensures that the transformed matrix/vectorhas the same sum as the original one.To summarize, the transformation process involves three steps:1. Computing the scaling factor by dividing each element in the originalmatrix/vector by the sum of all its elements.2. Computing the element-wise product between the original matrix/vectorand the scaling factor.3. Applying the element-wise product recursively to all layers of the originalmatrix/vector.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average