**A short note on how the Graph Laplacian is a natural analogue of the Laplacian in vector analysis.**

For a twice differentiable function on the euclidean space, the Laplacian of , is the div(grad()) or the divergence of the gradient of . Let’s consider the laplacian in two dimensions:

To get an intuition about what it does, it is useful to consider a finite difference approximation to the above:

The above makes it clear that the Laplacian behaves like a ** local averaging operator**. Infact the above finite difference approximation is used in image processing (say, when h is set to one pixel) to detect sharp changes such as edges, as the above is close to zero in smooth image regions.

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The finite difference approximation intuitively suggests that a discrete laplace operator should be natural. At the same time, the Graph Theory literature defines the Graph Laplacian as the matrix (where is the degree matrix and the adjacency matrix of the graph , both are defined below). It is not obvious that this matrix follows from the div(grad(f)) definition mentioned above. The purpose of this note is to explicate in short on how this is natural, once the notion of gradient and divergence operators have been defined over a graph.

First of all the two most natural matrices that can be associated with a graph are defined below:

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There are two natural matrices that are used to specify a simple graph with vertex set and edge set : The Adjacency matrix and the Incidence matrix .

The Adjacency matrix is defined as a matrix such that:

To define the incidence matrix , we need to fix an orientation of the edges (for our purpose an arbitrary orientation suffices), then:

Now suppose we have a real valued function over , . For , we view this as a column vector indexed by the vertices.

Similarly let be a real valued function over the edges , (again, we view this as a row vector indexed by the edges).

Now, consider the following map: (which is now a vector indexed by ). The value of this map at any edge is simply the difference of the values of at the two end points of the edge i.e. . This makes it somewhat intuitive that the matrix is some sort of a difference operator on . Given this “difference operator” or “discrete differential” view, can then be viewed as the * gradient *that measures the change of the function along the edges of . Thus the map is just the

**gradient operator.**Like the above, now we consider the following map: (which is now a vector indexed by ). Recall that was defined on the edges. The value of this map at any vertex is . If we were to view as describing some kind of a flow on the edges, then is the *net outbound flow* on the vertex , which is just the * divergence*. Thus the map is just the

*.*

**divergence operator**Now consider (note that this makes sense since is a column vector), going by the above, this should be the * divergence of the gradient*. Thus, the analogy with the “real” Laplacian makes sense and the matrix is appropriately called the Graph Laplacian.

Using the definition of , a simple computation yields the following for :

Looking at the above and recalling the definition for the adjacency matrix , it is easy to see that , where is the diagonal matrix with . This is just the familiar definition that I mentioned at the start.

PS: From the above it is also clear that the Laplacian is positive semidefinite. Indeed, consider , which is written as

on October 4, 2015 at 2:35 pm |Nihal v swonderful

on January 7, 2020 at 9:22 pm |AndreaClear! but I don’t understand why \nabla^T g is the divergence operator…the classical divergence definition is very different…