During the summer and fall of 2016, Annie Rak did some URAF (a program formerly called HCRP) on partial differential equations on graphs. It led to a senior thesis in the applied mathematics department. Here is a project page and here [PDF] were some notes from the summer. The research of Annie mostly dealt with advection models on directed graphs (digraphs).
As partial differential equations in general relate to diffusion process, this happens also here. The stochastic process is the Markov dynamics defined on the graphs. The advection model for a directed graph is , where is the directed Laplacian. As in the usual Laplacian for undirected graphs which leads to the heat equation , the advection Laplacian uses difference operators: the modification of the gradient which is the minimum of and . The divergence as well as are defined by the usual exterior derivative . The consensus model is the situation, where the graph is replaced by its reverse graph , where all directions are reversed. One can therefore easily focus on advection. The advection dynamics relates to Markov chains: If , then is a left stochastic matrix, a Markov operator, which maps probability vectors to probability vectors. The kernel of is related to the fixed points of . Assume , then and . Perron-Frobenius allows to study the structure of the equilibria or equivalently the kernel of .
Lets look at the kite graph. The Laplacian of is = – , where is the incidence matrix. When equipped with an orientation is a digraph. Given the orientation, the incidence matrix is the matrix ${\rm div} = D_{a,e(b)}$ which is if the arrow between points towards and else. It is the discrete analogue of the divergence. The matrix is the gradient. Now, is the Laplacian: =.
The gradient of the directed graph contains only the incoming entries. The modified Laplacian is . Again it is of the form , where is the diagonal incoming vertex degree diagonal matrix and is the adjacency matrix if .
Both matrices and have the property that the sum of each column entries is constant . This means that have the eigenvector with eigenvalue . So, also has an eigenvalue .
We can write , where is the diagonal part and the off diagonal part. Now, in the case when all incoming vertex degrees are nonzero, we can invert and form . This is a stochastic matrix in the sense that all entries are non-negative in and that the sum over all column entries is equal to . These are now transition probabilities.
The heat equation is . The wave equation is . The advection equation is . Now, while is symmetric and has non-negative real eigenvalues, the matrix can have complex eigenvalues. In the kite example, the eigenvalues of are $\{4,4,2,0 \}$, the eigenvalues of the directed graph with a loop are , the spectrum of is .