I am still in shock at the news that Microsoft decided to shut down MSR SVC and fire, literally from one day to the next, almost all the scientific staff.

It is shocking that the company decided that it was a good idea to destroy an investment they had made over several years; I am only familiar with the theory side of MSR SVC, which had great success in nurturing young talent and developing breakthrough ideas and results. The work on privacy led by Cynthia Dwork, for example, informed the thinking on privacy of higher management, and on how one could find new ways to balance European privacy law with the data collection necessary for advertising. This is one of the returns of having academic research in a company: not so that they can implement in C++ their algorithms, but so that they can generate and communicate new ways of thinking about problems. (Cynthia is one of the few people retained by Microsoft, but she has lost all her collaborators.) Microsoft’s loss will be other places’ win as the MSR SVC diaspora settles down elsewhere.

It is also shocking that, instead of planning an orderly shutdown, they simply threw people out overnight, which shows a fundamental indifference to the way the academic job market works (it can take a full year for an academic job offer to materialize).

I am not shocked by the class demonstrated by Omer Reingold; the moral stature of people is best seen in difficult moments. Omer has written a beautiful post about the lab, whose comment section has become a memorial to the lab, with people posting their personal remembrances.

Here at Berkeley and Stanford we will do our best to help, and we will make sure that everybody has some space to work. There will also be some kind of community-wide response, but it will take some time to figure out what we can do. Meanwhile, I urge everybody to reach out to their friends formerly at MSR SVC, make them feel the love of their community, and connect them to opportunities for both short term and long term positions, as they weigh their options.

Suppose that we want to construct a very good family of {d}-regular expander graphs. The Alon-Boppana theorem says that the best we can hope for, from the point of view of spectral expansion, is to have {\lambda_2 \leq 2 \sqrt{d-1}}, and we would certainly be very happy with a family of graphs in which {\lambda_2 \leq O(\sqrt d)}.

Known constructions of expanders produce Cayley graphs (or sometimes Schreier graphs, which is a related notion), because it is easier to analyze the spectra of such graphs. If {\Gamma} is a group with operation {\cdot} and {a^{-1}} is the inverse of element {a}, and {S} is a symmetric set of generators, then the Cayley graph {Cay(\Gamma, S)} is the graph whose vertices are the elements of {\Gamma} and whose edges are the pairs {(a,b)} such that {a\cdot b^{-1} \in S}.

When the group is Abelian, there is good news and bad news. The good news is that the eigenvectors of the graphs are completely characterized (they are the characters of {\Gamma}) and the eigenvalues are given by a nice formula. (See here and here.) The bad news is that constant-degree Cayley graphs of Abelian groups cannot be expanders.

That’s very bad news, but it is still possible to get highly expanding graphs of polylogarithmic degree as Cayley graphs of Abelian groups.

Here we will look at the extreme case of a family of graphs of degree {d_n = n/2}, where {n} is the number of vertices. Even with such high degree, the weak version of the Alon-Boppana theorem still implies that we must have {\sigma_2 \geq \Omega(\sqrt d_n)}, and so we will be happy if we get a graph in which {\sigma_2 \leq O(\sqrt d) = O(\sqrt n)}. Highly expanding graphs of degree {n/2} are interesting because they have some of the properties of random graphs from the {G_{n,\frac 12}} distribution. In turn, graphs from {G_{n,\frac 12}} have all kind of interesting properties with high probabilities, including being essentially the best known Ramsey graphs and having the kind of discrepancy property that gives seedless extractors for two independent sources. Unfortunately, these properties cannot be certified by spectral methods. The graph that we will study today is believed to have such stronger properties, but there is no known promising approach to prove such conjectures, so we will content ourselves with proving strong spectral expansion.

The graph is the Paley graph. If {p} is a prime, {{\mathbb Z} / p {\mathbb Z}} is the group of addition modulo {p}, and {Q} is the set of elements of {{\mathbb Z}/ p{\mathbb Z}} of the form {r^2 \bmod p}, then the graph is just {Cay ( {\mathbb Z}/p{\mathbb Z}, Q)}. That is, the graph has a vertex {v} for each {v\in \{ 0,\ldots,p-1\}}, and two vertices {a,b} are adjacent if and only if there is an {r\in \{ 0,\ldots,p-1\}} such that {a-b \equiv r^2 \pmod p}.

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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}.

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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.

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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.

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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.

Please, do better in the comments!

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.

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