Graph Fourier transform definition

Question

I have a question about the definition of the graph Fourier transform. Let me start with definition.

Let $A$ be the adjacency matrix of a graph $G$ with vertex set $V = \{1, 2, \dots, n\}$. The Laplacian matrix of $G$ is defined as $L = D - A$, where $D$ is a diagonal degree matrix with $d_{ii} = deg(i)$. Let $\varphi_1, \dots, \varphi_n$ be an orthonormal eigenbasis of $L$ and $\lambda_1, \dots, \lambda_n$ be the corresponding eigenvalues. Let $f$ be a $V \rightarrow \mathbb{R}$ function. The graph Fourier transform is defined as \begin{equation} \hat{f}(\lambda_i) = \langle f, \varphi_i \rangle = \sum\limits_{k = 1}^n f(k) \varphi_i^*(k) \end{equation}

My question is: what happens if $\lambda_i = \lambda_{i + 1}$? It seems to me that this definition could give two different values for $\hat{f}(\lambda_i)$. Is it guaranteed that $\sum\limits_{k = 1}^n f(k) \varphi_i^*(k) = \sum\limits_{k = 1}^n f(k) \varphi_{i+1}^*(k)$?

EDIT: I have no problem with choosing an orthonormal eigenbasis even if there are eigenvalues with multiplicity bigger than $1$. My problem arises only after the eigenvectors are chosen. Suppose that $\lambda_i = \lambda_{i + 1} = 3$. Then I have two different formulas for $\hat{f}(3)$:

\begin{equation} \hat{f}(3) = \sum\limits_{k = 1}^n f(k) \varphi_i^*(k) \end{equation} and \begin{equation} \hat{f}(3) = \sum\limits_{k = 1}^n f(k) \varphi_{i+1}^*(k) \end{equation}

I think there are two possibilities:

1. The two quantities above are the same: $\sum\limits_{k = 1}^n f(k) \varphi_i^*(k) = \sum\limits_{k = 1}^n f(k) \varphi_{i+1}^*(k)$. I can't see why this would be true.

2. I misunderstand something about the definition.

Answer 1

I saw the definition I mentioned in the question in many places. (E.g. here.) In Graph Structured Data Viewed Through a Fourier Lens the definition is different: \begin{equation} \hat{f}(i) = <f, \varphi_i> = \sum\limits_{k = 1}^n f(k) \varphi_i^*(k) \end{equation} This answers my question. :)