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
.