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In the ideal case, when each node has one or two edges, this is the only valid interpretation of the graphs.
For better interpretation of the graphs we show the 34 ICF categories together with a short explanation in Table 1.
This is because the interpretation of the graphs is predicated on the x-axis being a linear scale.
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The eigenvectors and eigenvalues of the Laplacian matrix provide a spectral interpretation of the graph signal.
To facilitate interpretation of the graph, we show the bribe offered as a percent of the maximum bribe (9 ECU).
If people are systematic about the feature that they attend to first, then it is likely that their interpretation of the graph is driven by that guiding feature.
Thus, when a graph can be interpreted in multiple ways, people attend to a single dimension (e.g., size) in a specific order (e.g., taller bar first), which may generate a specific interpretation of the graph (e.g., "the taller bar is on the left").
Second, the interpretation of the graph p is limited as the reported p-values are not adjusted for multiple testing.
By default, each mapped aesthetic defined inside aes produces a guide to aid interpretation of the graph: for positional aesthetics, the guide is a scaled axis, whereas for non-positional aesthetics, the default guide is typically a legend.
They follow from an interpretation of the graph as a Markov chain (Kemeny and Snell, 1983) characterized by a transition probability matrix P. The probability of transition from node i to node j is given by where w ij denotes the weight assigned to the edge i → j.
However, the interpretation of these graphs (how certain clusters relate to projects or groups) requires domain and organizational knowledge.
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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