Spectral theory of graph Laplacian besides $\lambda_2$

I came across some exciting references to 'holistic spectral data' while watching this great talk by prof. James Lee at UWashington, so it might be worth giving a look. The slides are here. A little search turned up some interesting results:

1. At arount 28:00 in the talk, he mentions that if $\lambda_i$ and $v_i$ are the eigenvalues (in increasing order) and eigenvectors of the Laplacian, and $g_i$ are random $\mathcal{N}(0,1)$ Gaussians, we have $$\text{cover time of $G$} \asymp |V| \left\|\sum_{i=2}\frac{g_i}{\sqrt{\lambda_i}}v_i \right\|_\infty $$ in the sense of tight concentration. Wow! The quantity in the infinity norm, Lee claims, is called the Gaussian mean field and is useful in other contexts.
2. At around 30:00 he continues discussing 'holistic spectral data', mentioning how the above spectral characterization helps us prove things about cover time. Examples from electrical networks follow.
3. In the paper http://arxiv.org/abs/1111.1055, Lee, Gharan and Trevisan resolve a conjecture saying that there are $k$ eigenvalues close to zero, the graph can be partitioned into $k$ parts with small cuts between them (this was alluded to in the comments).

Besides the already mentioned article by Lee, Gharan and Trevisan, let me mention two aspects of the theory of higher eigenvalues:

• The possibly earliest appearance of the graph Laplacian dates back to 1973: W.E. Donath, A.J. Hoffman, Lower bounds for the partitioning of graphs, IBM J. Res. Develop. The main theorem of that paper gives an estimate on the number of estimates that are necessary in order to cut the graph into $k$ parts in terms of a weighted sum of the $k$ largest eigenvalues of the Laplacian.

• E.B. Davies, G.M.L. Gladwell, J. Leydold and P.F. Stadler have written in 2001 an article where Courant's nodal domain theorem is extended to graphs: Discrete nodal domain theorems, Lin. Alg. Appl.

One thing I've learned after asking this question is spectral coordinates and the analogy with Fourier analysis. From a blog post:

The eigenvectors associated with the smallest eigenvalues of the graph Laplacian are analogous to low frequency sines and cosines. The eigenvalue components corresponding to nearly vertices in a graph should be close together. This analogy explains why spectral coordinates work so well.