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Weighted out-degree

As explained in Graph Algorithms for Data Science

Weighted out-degree is the sum of the weights of a node's outgoing relationships in a directed weighted network.

Weighted out-degree sums the weights of a node’s outgoing relationships. In a retweet amplification network, it measures how much content a user distributes, not merely how many users they reach. section 6.2.1

GDS degree centrality supports weighted calculations through relationshipWeightProperty. Set it to weight when analyzing the projected graph. section 6.2.1

Retweet amplification

The weighted distribution has a mean of 4.7, a median of 1, and a maximum of 2,006. A small number of extreme users therefore raise the average; the highest values may indicate automated retweeting. section 6.2.1

How Graph Algorithms for Data Science covers it

How deep it goes
Explains weighted interpretation, distribution statistics, outliers, and GDS configuration, but does not detail the full query for ranking nodes.
Examples
Uses retweet counts, distribution percentiles, automated-retweet hypotheses, and GDS degree statistics.
What you can do after
Operate/configure GDS degree statistics with relationshipWeightProperty to evaluate weighted out-degree distributions.
How it connects
Connects degree centrality with directed networks, in-degree, relationship weights, and retweet amplification networks.
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.