Assertion: graph | /r/HasProperty | laplacian
Facets:
Source sentences:
text: But as useful as the adjacency matrix is, there’s another matrix you can associate with a graph that receives tons of attention in spectral graph theory: the graph Laplacian.
text: In fact, if you’re not inclined to think about continuous systems of differential equations, you can still appreciate the graph Laplacian.
text: If you like the gradient idea from earlier, you should think of the graph Laplacian as a matrix that is encoded with the process of computing gradients and gradient-norms for arbitrary functions on .
text: A priori, one might wonder why you could hope to extract information from the eigenvectors and eigenvalues of the graph Laplacian, and