Why Laplacian matrix needs normalization and how come the sqrt-power of degree matrix? The symmetric normalized Laplacian matrix is defined as $$\ L = D^{1/2}AD^{-1/2}$$ where L is Laplacian matrix, A is adjacent matrix. Element $A_{ij}$ represents a measure of the similarity between data points with indices $i$ and $j$. D is diagonal matrix, defined as $\ D = \sum\limits_i A_{ij}$. In other words, it is the sum of similarity from node $j$ to all its neighbor node $i$.

I can't figure out some question about the formula:

  • Why we need to normalize Laplacian Matrix like that? $\ {1/2}$-power ?
  • Is there any special reason?
  • Is there any references?
  • $\begingroup$ Note that "Laplacian" tag refers to a differential operator, where you seem to have in mind its use in (undirected) graph algorithms, where it is more of a difference operator. You should supply more context, because the answer to "why" one wants to define the symmetric normalized Laplacian matrix as shown depends on the purpose to which it is put. $\endgroup$
    – hardmath
    Jan 21, 2015 at 13:26
  • $\begingroup$ Perhaps a way to frame your Question is, what are the eigenvalues of the Laplacian matrix in comparison with the "symmetric normalized" Laplacian matrix? $\endgroup$
    – hardmath
    Jan 21, 2015 at 13:29
  • $\begingroup$ In Spectral clustering, a graph is been transformed to three matrix. 1. A Adjacent matrix. 2. D diagonal matrix, $d_{ii}$ represents the degree of node $i$. 3. L Laplacian where $L = D - W$. And we calculate the eigenvector of L. And we choice $m$ eigenvector where one eigenvector represents as one axis, which means $m$ eigenvector equals to the new coordination of the data point, for example,has a E the eigenvector matrix, row represent data $i$, col represents eigenvector. Then, we use k-means clustering data on the new coordination. There is no specific definition of eigenvalues. $\endgroup$
    – John Buger
    Jan 21, 2015 at 13:41
  • $\begingroup$ L laplacian matrix is be used as a data reduction tool in spectral clustering. $\endgroup$
    – John Buger
    Jan 21, 2015 at 13:47
  • 2
    $\begingroup$ I think you have omitted a minus sign in the exponent of the first matrix factor. Should it be $D^{-1/2} $? $\endgroup$
    – hardmath
    Jan 22, 2015 at 17:30

3 Answers 3


Unnormalized Laplacian serve in the approximation of the minimization of RatioCut, while normalized Laplacian serve in the approximation of the minimization of NCut.

Basically, in the unnormalized case, you optimize an objective relative to the number of nodes in each cluster. And, in the normalized case, you optimize the objective relative to the volume of each cluster.

The square root comes from this: $f^\top(I-D^{-1}A)f = g^\top(I-D^{-1/2}AD^{-1/2})g$ where $g=D^{1/2}f$.

So, if your optimization method relies on a symmetric PSD matrix, you use the symmetric normalized Laplacian, and then remove the bias by computing $D^{-1/2}g$ to recover $f$.

More insight about RatioCut and NCut.

Let $G=(V,E)$ be a graph with vertex set $V=\{v_1,\dots,v_n\}$ and edge set $E$, $w_{ij}$ denote the positive weight on the edge between $v_i$ and $v_j$. Let $\{C_i:1 \le i \le k\}$ be a disjoint partition of $V$. Define $$ Cut(C_i:1\le i\le k)\triangleq\frac{1}{2}\sum_{c=1}^k \sum_{i \in C_c,j\in \bar C_c}A_{ij} $$ $$ RatioCut(A_i:1\le i \le k)\triangleq\sum_{i=1}^k\frac{Cut(A_i,\bar A_i)}{|A_i|} $$ $$ NCut(A_i:1\le i \le k)\triangleq\sum_{i=1}^k\frac{Cut(A_i,\bar A_i)}{vol(A_i)} $$

RatioCut: Let $$\tag{1}\label{1} f_i=\begin{cases}\sqrt{|\bar A|/|A|} & \text{if } v_i\in A\\\sqrt{|A|/|\bar A|} & \text{if } v_i \in \bar A\end{cases} $$

\begin{align*} f^\top L f & = \frac{1}{2}\sum_{i,j=1}^n w_{ij}(f_i-f_j)^2 \\ & = \frac{1}{2}\sum_{i\in A,j\in \bar A}w_{ij}\left(\sqrt{\frac{|\bar A|}{|A|}}+\sqrt{\frac{|A|}{|\bar A|}}\right) + \frac{1}{2}\sum_{i\in \bar A,j\in A}w_{ij}\left(-\sqrt{\frac{|\bar A|}{|A|}}-\sqrt{\frac{|A|}{|\bar A|}}\right) \\ &=Cut(A,\bar A)\left(\frac{|\bar A|}{|A|}+\frac{|A|}{|\bar A|}+2\right)\\ &=Cut(A,\bar A)\left(\frac{|A|+|\bar A|}{|A|}+\frac{|A|+|\bar A|}{|\bar A|}\right)\\ &=|V| RatioCut(A,\bar A) \end{align*}

You can also see that $\sum_{i=1}^n f_i=0$, so $f\perp \mathbb 1$.

So minimizing RatioCut is equivalent to the following problem: $$ \min_{A\subset V}f^\top L f\text{ subject to $f\perp\mathbb 1$, $f_i$ as defined in Eq.\eqref{1}},\|f\|^2=n $$

NCut: For this case, define $$\tag{2}\label{2} f_i=\begin{cases} \sqrt{\frac{vol(\bar A)}{vol(A)}} &\text{if $v_i\in A$}\\ \sqrt{\frac{-vol(A)}{vol(\bar A)}} &\text{if $v_i\in \bar A$}\\ \end{cases} $$

Similar to above, we have $Df\perp \mathbb 1$, $f^\top D f=vol(V)$ and $f^\top L f=vol(V)NCut(A,\bar A)$. Minimizing NCut is equivalent to $$ \min_{A\subset V} f^\top L f \text{ subject to $f$ as in Eq.\eqref{2}, $Df\perp \mathbb 1$ and $f^\top Df=vol(V)$} $$ Substituting $g=D^{1/2}f$ we get $$ \min_{A\subset V} g^\top D^{-1/2}LD^{-1/2} g \text{ subject to $g=D^{1/2}f$, $f$ as in Eq.\eqref{2}, $g\perp D^{1/2}\mathbb 1$ and $\|g\|^2=vol(V)$} $$

Then observe that $D^{-1/2}LD^{-1/2}$ is the symmetric normalized Laplacian.

  • $\begingroup$ Why $D^{1/2}$ ? Why not $D^{1/3}$ or something else? $\endgroup$
    – John Buger
    Jan 21, 2015 at 14:43
  • 1
    $\begingroup$ Because $D^{-1}A$ is the transition matrix of the underlying markov chain. Whereas $D^{-2/3}A$ is nothing like that. $\endgroup$
    – davcha
    Jan 21, 2015 at 14:57
  • $\begingroup$ Sorry my poor math...can it be more detail? Why can $D^{1/2}$ represents the transform operation from RatioCut to NCut? $\endgroup$
    – John Buger
    Jan 21, 2015 at 15:09
  • $\begingroup$ I'm no longer in front of a computer. I'll edit my answer later to give you more details. $\endgroup$
    – davcha
    Jan 21, 2015 at 15:32
  • $\begingroup$ Why $f\perp \mathbb 1$? $\sum_{i=1}^n f_i=0$? Why we need to keep the property? Why we need constraints $\|f\|^2=\sqrt{n}$ ? $\|g\|^2=vol(V)$? $\endgroup$
    – John Buger
    Jan 22, 2015 at 13:22

The notion of Normalised Laplace matrix was given by Fan R. K. Chung in her book Spectral Graph Theory. Two basic reasons of its widely acceptance is that its eigenvalues are consistent with the eigenvalues in the spectral geometry and in stochastic processes, and that many results which were only known for regular graphs can be generalised for all graphs. The Laplacian $L=D-A$ works well for the regular graphs but the Normalised laplacian $ℒ=D^{-1/2}LD^{1/2} =D^{-1/2}(D-A)D^{1/2}=I-D^{-1/2}AD^{1/2}$ not only works well for regular but also irregular graphs. First few pages of the book by Chung will answer your question in detail.

  • $\begingroup$ the equation is not correct. a minus (-) is missing in the second D^(1/2) $\endgroup$
    – seralouk
    Jan 29, 2021 at 9:21
  • $\begingroup$ should be D^(-1/2) $\endgroup$
    – seralouk
    Mar 13, 2021 at 7:33

Note that relaxed version of NCut problem is that of minimization of generalized eigenproblem

$Lx = \lambda Dx$

consult the paper by Shi and Malik for a justification.

To solve this problem, i.e. to find eigenvector $x$ and eigenvalue $\lambda$, multiply both sides of this equation by $D^{-1/2}$:

$D^{-1/2}Lx=\lambda D^{1/2}x$

This equation can be rewritten as

$D^{-1/2}LD^{-1/2}D^{1/2}x=\lambda D^{1/2}x$

Setting $y=D^{1/2}x$ you obtain

$D^{-1/2}LD^{-1/2}y=\lambda y$

that is you reduced the generalized eigenproblem $Lx=\lambda Dx$ to the standard eigenproblem $\mathcal{L} y = \lambda y$, where $\mathcal{L} = D^{-1/2}LD^{-1/2}$ is the normalized Laplacian matrix.

Knowing eigenvector $y$ you can find the eigenvector $x=D^{-1/2}y$.

  • $\begingroup$ I think your first equation is already wrong since the right side of your equation is missing a - sign in the exponent of D $\endgroup$
    – v.tralala
    Jan 3, 2023 at 17:42

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