We are stuck with understanding the figure from the paper, shown below (link)
What is the idea of each of 8 graphs?
- Is it smth like 1st eigenvector $v_1 = (a_1,a_2,…)$ where $a_i$ - negative values, ...etc and similarly last eigenvector (8th) $v_8 = ( b_1,b_2,…)$ where $b_i$ - either positive or negative values( if we look at picture)?
Why assignment of values is less and less uniform?
My understanding so far is: The bigger the eigenvalue is, then the corresponding eigenvector accounts for the most non typical, big in value, ( maybe opposite values) of the vertexes, like in the very right graph. I.e. my feeling is that it is something like PCA, where the biggest eigenvalue is responsible for biggest variance of data. But my understanding is like a sieve.