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Master1022

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- What does the eigenvector of the laplacian matrix actually represent?

Hi,

I was reading the following book about applying deep learning to graph networks:

[tex] L = D - A [/tex]

where ##D## is the degree matrix (it is diagonal) and ##A## is the adjacency matrix.

What does an eigenvector of a Laplacian graph actually represent on an intuitive level?

Also, I apologize if this is the wrong forum - should I have posted elsewhere?

Thanks in advance.

I was reading the following book about applying deep learning to graph networks:

__link__. In chapter 2 (page 22), they introduce the graph Laplacian matrix ##L##:[tex] L = D - A [/tex]

where ##D## is the degree matrix (it is diagonal) and ##A## is the adjacency matrix.

**Question:**What does an eigenvector of a Laplacian graph actually represent on an intuitive level?

Also, I apologize if this is the wrong forum - should I have posted elsewhere?

Thanks in advance.