Let ${G=(V,E)}$ be a ${d}$-regular graph, and let

$\displaystyle d= \lambda_1 \geq \lambda_2 \geq \cdots \geq \lambda_n$

be the eigenvalues of the adjacency matrix of ${A}$ counted with multiplicities and sorted in descending order.

How good can the spectral expansion of ${G}$ be?

1. Simple Bounds

The simplest bound comes from a trace method. We have

$\displaystyle trace(A^2) = \sum_i \lambda_i^2$

by using one definition of the trace and

$\displaystyle trace(A^2) = \sum_{v,v} (A^2)_{v,v} \geq dn$

using the other definition and observing that ${(A^2)_{v,v}}$ counts the paths that go from ${v}$ to ${v}$ in two steps, of which there are at least ${d}$: follow an edge to a neighbor of ${v}$, then follow the same edge back. (There could be more if ${G}$ has multiple edges or self-loops.)

So we have

$\displaystyle dn \leq d^2 + \sum_{i=2,\ldots,n} \lambda_i ^2$

and so

$\displaystyle \max_{i=2,\ldots,n} |\lambda_i | \geq \sqrt d \cdot \sqrt{\frac {n-d}{n-1}}$

The condition ${d \leq n(1-\epsilon)}$ is necessary to get lower bounds of ${\Omega(\sqrt d)}$; in the clique, for example, we have ${d=n-1}$ and ${\lambda_2 = \lambda_n = -1}$.

A trace argument does not give us a lower bound on ${\lambda_2}$, and in fact it is possible to have ${\lambda_2=0}$ and ${d= n/2}$, for example in the bipartite complete graph.

If the diameter of ${G}$ is at least 4, it is easy to see that ${\lambda_2 \geq \sqrt d}$. Let ${a,b}$ be two vertices at distance 4. Define a vector ${x}$ as follows: ${x_a = 1}$, ${x_v = 1/\sqrt d}$ if ${v}$ is a neighbor of ${a}$, ${x_b=-1}$ and ${x_v = - 1/\sqrt d}$ if ${v}$ is a neighbor of ${b}$. Note that there cannot be any edge between a neighbor of ${a}$ and a neighbor of ${b}$. Then we see that ${||x||^2 = 4}$, that ${x^T A x \geq 4\sqrt d}$ (because there are ${2d}$ edges, each counted twice, that give a contribution of ${1/\sqrt d}$ to ${\sum_{u,v} A_{uv} x_u x_v}$) and that ${x}$ is orthogonal to ${(1,\ldots,1)}$.

2. Nilli’s Proof of the Alon-Boppana Theorem

Nilli’s proof of the Alon-Boppana theorem gives

$\displaystyle \lambda_2 \geq 2 \sqrt{ d-1 } - O \left( \frac {\sqrt{d-1}}{diam(G)-4} \right)$

where ${diam(G) \geq \frac {\log |V|}{\log d-1}}$ is the diameter of ${G}$. This means that if one has a family of (constant) degree-${d}$ graphs, and every graph in the family satisfies ${\lambda_2 \leq \lambda}$, then one must have ${\lambda \geq 2 \sqrt{d -1}}$. This is why families of Ramanujan graphs, in which ${\lambda_2 \leq 2 \sqrt{d-1}}$, are special, and so hard to construct, or even to prove existence of.

Friedman proves a stronger bound, in which the error term goes down with the square of the diameter. Friedman’s proof is the one presented in the Hoory-Linial-Wigderson survey. I like Nilli’s proof, even if it is a bit messier than Friedman’s, because it starts off with something that clearly is going to work, but the first two or three ways you try to establish the bound don’t work (believe me, I tried, because I didn’t see why some steps in the proof had to be that way), but eventually you find the right way to break up the estimate and it works.

So here is Nilli’s proof. Read the rest of this entry »

Today, after a lecture in the spectral graph theory boot camp at the Simons institute, I was asked what the expander mixing lemma is like in graphs that are not regular.

I don’t know if I will have time to return to this tomorrow, so here is a quick answer.

First, for context, the expander mixing lemma in regular graph. Say that ${G=(V,E)}$ is a ${d}$-regular undirected graph, and ${A}$ is its adjacency matrix. Then let the eigenvalues of the normalized matrix ${\frac 1d A}$ be

$\displaystyle 1 = \lambda_1 \geq \lambda_2 \geq \cdots \geq \lambda_n$

We are interested in graphs for which all the eigenvalues are small in absolute value, except ${\lambda_1}$, that is, if we define

$\displaystyle \sigma_2 := \max\{ |\lambda_2|, |\lambda_3 | , \ldots , | \lambda_n | \}$

we are interested in graphs for which ${\sigma_2}$ is small. The expander mixing lemma is the fact that for every two disjoint sets ${S}$ and ${T}$ of vertices we have

$\displaystyle \left | E(S,V-S) - \frac d n \cdot |S| \cdot |T| \right | \leq \sigma_2 d \sqrt{ |S| \cdot |T|} \ \ \ \ \ (1)$

The inequality (1) says that, if ${\sigma_2}$ is small, then the number of edges between every two large sets of vertices is almost determined just by the size of the sets, and it is equal to the expected number of edges between the two sets in a random ${d}$-regular graph, up to an error term that depends on ${\sigma_2}$.

For the proof, we observe that, if we call ${J}$ the matrix that has ones everywhere, then

$\displaystyle \sigma_2 = \max_{x \perp (1,\ldots , 1) } \frac { |x^T A x|}{d\cdot x^Tx} = \max_{x} \frac { \left|x^T \left( A - \frac dn J \right) x \right|}{d\cdot x^T x} = \max_{x,y} \frac { \left|x^T \left( A - \frac dn J \right) y \right|}{d\cdot ||x|| \cdot ||y||}$

and then we substitute ${x := {\mathbf 1}_S}$ and ${y:= {\mathbf 1}_T}$ in the above expression and do the calculations.

In the case of an irregular undirected graph ${G=(V,E)}$, we are going to consider the normalized adjacency matrix ${M:= D^{-\frac 12} A D^{-\frac 12 }}$, where ${A}$ is the adjacency matrix of ${G}$ and ${D}$ is the diagonal matrix such that ${D_{v,v} = d_v}$, where ${d_v}$ is the degree of ${v}$. As in the regular case, the eigenvalues of the normalized adjacency matrix satisfy

$\displaystyle 1 = \lambda_1 \geq \lambda_2 \geq \cdots \lambda_n \geq -1$

Let us define

$\displaystyle \sigma_2:= \max \{ |\lambda_2| , |\lambda_3| , \ldots, |\lambda_n| \}$

the second largest eigenvalue in absolute value of ${M}$.

We will need two more definitions: for a set of vertices ${S}$, its volume is defined as

$\displaystyle vol(S):= \sum_{v\in S} d_v$

the sum of the degrees and ${\bar d = \frac 1n \sum_v d_v}$ the average degree, so that ${vol(V) = \bar d n }$. Now we have

Lemma 1 (Expander Mixing Lemma) For every two disjoint sets of vertices ${S}$, ${T}$, we have

$\displaystyle \left | E(S,V-S) - \frac {vol(S) \cdot vol(T) }{vol(V)} \right | \leq \sigma_2 \sqrt{vol(S) \cdot vol(T) } \ \ \ \ \ (2)$

So, once again, we have that the number of edges between ${S}$ and ${T}$ is what one would expect in a random graph in which the edge ${(u,v)}$ exists with probability ${d_ud_v/vol(V)}$, up to an error that depends on ${\sigma_2}$.

While preparing for the spectral graph theory boot camp, which starts this Tuesday at the Simons Institute, I collected the lecture notes of my class on graph partitioning and expanders in one file.

There are no references and, most likely, plenty of errors. If you use the notes and find mistakes, please let me know by either emailing luca at berkeley or leaving a comment here.

The spectral norm of the infinite ${d}$-regular tree is ${2 \sqrt {d-1}}$. We will see what this means and how to prove it.

When talking about the expansion of random graphs, abobut the construction of Ramanujan expanders, as well as about sparsifiers, community detection, and several other problems, the number ${2 \sqrt{d-1}}$ comes up often, where ${d}$ is the degree of the graph, for reasons that tend to be related to properties of the infinite ${d}$-regular tree.

A regular connected graph is Ramanujan if and only if its Ihara zeta function satisfies a Riemann hypothesis.

The purpose of this post is to explain all the words in the previous sentence, and to show the proof, except for the major step of proving a certain identity.

There are at least a couple of reasons why more computer scientists should know about this result. One is that it is nice to see a connection, even if just at a syntactic level, between analytic facts that imply that the primes are pseudorandom and analytic facts that imply that good expanders are pseudorandom (the connection is deeper in the case of the Ramanujan Cayley graphs constructed by Lubotzky, Phillips and Sarnak). The other is that the argument looks at eigenvalues of the adjacency matrix of a graph as roots of a characteristic polynomial, a view that is usually not very helpful in achieving quantitative result, with the important exception of the work of Marcus, Spielman and Srivastava on interlacing polynomials.

In preparation for the special program on spectral graph theory at the Simons Institute, which starts in a week, I have been reading on the topics of the theory that I don’t know much about: the spectrum of random graphs, properties of highly expanding graphs, spectral sparsification, and so on.

I have been writing some notes for myself, and here is something that bothers me: How do you call the second largest, in absolute value, eigenvalue of the adjacency matrix of a graph, without resorting to the sentence I just wrote? And how do you denote it?

I have noticed that the typical answer to the first question is “second eigenvalue,” but this is a problem when it creates confusion with the actual second largest eigenvalue of the adjacency matrix, which could be a very different quantity. The answer to the second question seems to be either a noncommittal “${\lambda}$” or a rather problematic “${\lambda_2}$.”

For my own use, I have started to used the notation ${\lambda_{2abs}}$, which can certainly use some improvement, but I am still at a loss concerning terminology.

Perhaps one should start from where this number is coming from, and it seems that its important property is that, if the graph is ${d}$ regular and has ${n}$ vertices, and has adjacency matrix A, this number is the spectral norm of ${A - \frac dn J}$ (where ${J}$ is the matrix with ones everywhere), so that it measures the distance of ${A}$ from the “perfect ${d}$-regular expander” in a norm that is useful to reason about cuts and also tractable to compute.

So, since it is the spectral norm of a modification of the adjacency matrix, how about calling it ${<}$adjective${>}$ spectral norm? I would vote for shifted spectral norm because I would think of subtracting ${\frac dn J}$ as a sort of shift.

This year, perhaps because of a mistake, the winners of the Field Medals and the Nevanlinna prize were made public before the opening ceremony of the ICM.

Congratulations to my former colleague Maryam Mirzakhani for being the first Fields Medals winner from Iran, a nation that can certainly use some good news, and a nation that has always done well in identifying and nurturing talent in mathematics and related fields. She is also the first woman to receive this award in 78 years.

And congratulations to Subhash Khot for a very well deserved Nevanlinna prize, and one can read about his work in his own words, in my words, and about the latest impact of his work in the the words of Barak and Steurer.

The Simons foundations has excellent articles up about their work and the work of Artur Avila, Manjul Bhargava, and Martin Hairer, the other Fields Medal recipient. An unusual thing about Manjul Bhargava’s work is that one can actually understand the statements of some of his results.

The New York Times has a fascinating article according to which the Fields Medal got its current status because of Steve Smale and cold war paranoia. I don’t know if they are overstating their case, but it is a great story.

Shafi Goldwasser sends a reminder that the deadline to submit to the next innovations in theoretical computer science conference is next Friday. The conference will take place in January 2015 at the Weizmann Institute, with contingency plans to hold it in Boston in case the need for a relocation arises.

A few weeks ago, the Proceedings of the National Academy of Science published an article on a study conducted by a group of Cornell researchers at Facebook. They picked about 600,000 users and then, for a week, a subset of them saw fewer “negative” posts (up to 90% were filtered) than they would otherwise see, a subset saw fewer “positive” posts (same), and a control group got a random subset.

After the week, the users in the “negative” group posted fewer, and more negative, posts, and those in the “positive” group posted more, and more positive, posts.

Posts were classified according to an algorithm called LIWC2007.

The study run contrary to a conventional wisdom that people find it depressing to see on Facebook good things happening to their friends.

The paper has caused considerable controversy for being a study with human subjects conducted without explicit consent. Every university, including of course Cornell, requires experiments involving people to be approved by a special committee, and participants must sign informed consent forms. Facebook maintains that the study is consistent with its terms of service. The highly respected privacy organization EPIC has filed a complaint with the FTC. (And they have been concerned with Facebook’s term of service for a long time.)

Here I would like to explore a different angle: almost everybody thinks that observational studies about human behavior can be done without informed consent. This means that if the Cornell scientists had run an analysis on old Facebook data, with no manipulation of the feed generation algorithm, there would not have been such a concern.

At the same time, the number of posts that are fit for the feed of a typical user vastly exceed what can fit in one screen, and so there are algorithms that pick a rather small subset of posts that are evaluated to be of higher relevance, according to some scoring function. Now suppose that, if N posts fit on the screen, the algorithm picks the 2N highest scoring posts, and then randomly picks half of them. This seems rather reasonable because the scoring function is going to be an approximation of relevance anyway.

The United States has roughly 130 million Facebook subscriber. Suppose that the typical user looks, in a week, at 200 posts, which seems reasonable (in our case, those would be a random subset of roughly 400 posts). According to the PNAS study, roughly 50% of the posts are positive and 25% are negative, so of the initial 400, roughly 200 are positive and 100 are negative. Let’s look at the 100,000 users for which the random sampling picked the fewest positive posts: we would be expecting roughly 3 standard deviations below the mean, so about 80 positive posts instead of the expected 100; the 100,000 users with the fewest negative posts would get about 35 instead of the expected 50.

This is much less variance than in the PNAS study, where they would have got, respectively, only 10 positive and only 5 negative, but it may have been enough to pick up a signal.

Apart from the calculations, which I probably got wrong anyway, what we have is that in the PNAS study they picked a subset of people and then they varied the distribution of posts, while in the second case you pick random posts for everybody and then you select the users with the most variance.

If you could arrange distributions so that the distributions of posts seen by each users are the same, would it really be correct to view one study as experimental and one as observational? If the PNAS study had filtered 20% instead of 90% of the positive/negative posts, would it have been ethical? Does it matter what is the intention when designing the randomized algorithm that selects posts? If Facebook were to introduce randomness in the scoring algorithm with the goal of later running observational studies would it be ethical? Would they need to let people opt out? I genuinely don’t know the answer to these questions, but I haven’t seen them discussed elsewhere.

As of today, I am again an employee of the University of California, this time as senior scientist at the Simons Institute, as well as professor of EECS.

As anybody who has spent time there can confirm, the administrative staff of the Simons Institute is exceptionally good and proactive. Not only they take care of the things you ask them, but they take care of the things that you did not know you should have asked them. In fact at Berkeley the quality of the administration tracks pretty well the level at which it is taking place. At the level of departments and of smaller units, everything usually works pretty well, and then things get worse as you go up.

Which brings me to the office of the Chancellor, which runs U.C. Berkeley, and from which I received my official job offer. As you can see, that office cannot even get right, on its own letterhead, the name of the university that it runs:

Also, my address was spelled wrong, and the letter offered me the wrong position. I can’t believe they managed to put on the correct postage stamp. I was then instructed by the EECS department chair to respond by saying “I accept your offer of [correct terms],” which sounded passive-aggressive, but that’s what I did.