Networks and Matrices

Similar documents
COUNTING ROOTED FORESTS IN A NETWORK

This section is an introduction to the basic themes of the course.

The Matrix-Tree Theorem

A case study in Interaction cohomology Oliver Knill, 3/18/2016

18.312: Algebraic Combinatorics Lionel Levine. Lecture 19

ONE CAN HEAR THE EULER CHARACTERISTIC OF A SIMPLICIAL COMPLEX

1 Counting spanning trees: A determinantal formula

1.10 Matrix Representation of Graphs

MATHEMATICAL STRUCTURES IN GRAPH THEORY II. Oliver Knill Harvard University

ORIE 6334 Spectral Graph Theory September 8, Lecture 6. In order to do the first proof, we need to use the following fact.

Spectral Graph Theory and You: Matrix Tree Theorem and Centrality Metrics

Lecture 2: Ruelle Zeta and Prime Number Theorem for Graphs

Handout to Wu Characteristic Harvard math table talk Oliver Knill, 3/8/2016

Algebraic Methods in Combinatorics

ALTERNATING KNOT DIAGRAMS, EULER CIRCUITS AND THE INTERLACE POLYNOMIAL

Materials engineering Collage \\ Ceramic & construction materials department Numerical Analysis \\Third stage by \\ Dalya Hekmat

PROBLEMS ON LINEAR ALGEBRA

Lecture 13: Spectral Graph Theory

Lecture 1 and 2: Random Spanning Trees

Preliminaries and Complexity Theory

Algebraic Methods in Combinatorics

Unit 1 Matrices Notes Packet Period: Matrices

Kernels of Directed Graph Laplacians. J. S. Caughman and J.J.P. Veerman

Ma/CS 6b Class 20: Spectral Graph Theory

Strongly Regular Decompositions of the Complete Graph

18.S34 linear algebra problems (2007)

ON QUADRATIC ORBITAL NETWORKS

Laplacian eigenvalues and optimality: II. The Laplacian of a graph. R. A. Bailey and Peter Cameron

Combinatorial and physical content of Kirchhoff polynomials

Math 313 (Linear Algebra) Exam 2 - Practice Exam

Low Rank Co-Diagonal Matrices and Ramsey Graphs

Math 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

Riemann Hypotheses. Alex J. Best 4/2/2014. WMS Talks

THE THERMODYNAMIC FORMALISM APPROACH TO SELBERG'S ZETA FUNCTION FOR PSL(2, Z)

Acyclic Digraphs arising from Complete Intersections

LUCK S THEOREM ALEX WRIGHT

What is the Riemann Hypothesis for Zeta Functions of Irregular Graphs?

The Miracles of Tropical Spectral Theory

GROUPS AS GRAPHS. W. B. Vasantha Kandasamy Florentin Smarandache

4. Determinants.

MAC Module 3 Determinants. Learning Objectives. Upon completing this module, you should be able to:

Ma/CS 6b Class 20: Spectral Graph Theory

Spanning Trees and a Conjecture of Kontsevich

c c c c c c c c c c a 3x3 matrix C= has a determinant determined by

Spectra of Semidirect Products of Cyclic Groups

Sparsity of Matrix Canonical Forms. Xingzhi Zhan East China Normal University

HOMEWORK #2 - MATH 3260

Topics in Graph Theory

2.1 Laplacian Variants

An Introduction to Algebraic Graph Theory

Math 443/543 Graph Theory Notes 5: Graphs as matrices, spectral graph theory, and PageRank

Graduate Mathematical Economics Lecture 1

Chapter 3. Basic Properties of Matrices

RESISTANCE DISTANCE IN WHEELS AND FANS

1 T 1 = where 1 is the all-ones vector. For the upper bound, let v 1 be the eigenvector corresponding. u:(u,v) E v 1(u)

Nonnegative Matrices I

COUNTING AND ENUMERATING SPANNING TREES IN (di-) GRAPHS

1. General Vector Spaces

Linear Systems and Matrices

Chapter 2 Spectra of Finite Graphs

Notes on the Matrix-Tree theorem and Cayley s tree enumerator

The Structure of the Jacobian Group of a Graph. A Thesis Presented to The Division of Mathematics and Natural Sciences Reed College

PRODUCT DISTANCE MATRIX OF A GRAPH AND SQUARED DISTANCE MATRIX OF A TREE. R. B. Bapat and S. Sivasubramanian

Eigenvectors Via Graph Theory

Spanning Trees of Shifted Simplicial Complexes

1 Matrices and Systems of Linear Equations. a 1n a 2n

Mathematics. EC / EE / IN / ME / CE. for

Applying block intersection polynomials to study graphs and designs

An Introduction to Spectral Graph Theory

Identities for minors of the Laplacian, resistance and distance matrices

Chapter 2 - Basics Structures MATH 213. Chapter 2: Basic Structures. Dr. Eric Bancroft. Fall Dr. Eric Bancroft MATH 213 Fall / 60

Chapter 9: Relations Relations

Counting bases of representable matroids

MATH 2030: EIGENVALUES AND EIGENVECTORS

On minors of the compound matrix of a Laplacian

Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane.

Mathematical Diversity and Elegance in Proofs: Who will be the next Renaissance man?

LECTURE 4. PROOF OF IHARA S THEOREM, EDGE CHAOS. Ihara Zeta Function. ν(c) ζ(u,x) =(1-u ) det(i-au+qu t(ia )


Energy of Graphs. Sivaram K. Narayan Central Michigan University. Presented at CMU on October 10, 2015

An Introduction to Exponential-Family Random Graph Models

Dimension. Eigenvalue and eigenvector

Undergraduate Mathematical Economics Lecture 1

DISTINGUISHING GRAPHS WITH ZETA FUNCTIONS AND GENERALIZED SPECTRA

Conceptual Questions for Review

Chern forms and the Fredholm determinant

PALINDROMIC AND SŪDOKU QUASIGROUPS

ACYCLIC DIGRAPHS GIVING RISE TO COMPLETE INTERSECTIONS

Lecture J. 10 Counting subgraphs Kirchhoff s Matrix-Tree Theorem.

k=1 ( 1)k+j M kj detm kj. detm = ad bc. = 1 ( ) 2 ( )+3 ( ) = = 0

(a) If A is a 3 by 4 matrix, what does this tell us about its nullspace? Solution: dim N(A) 1, since rank(a) 3. Ax =

Problems for Putnam Training

FORMULAE FOR ZETA FUNCTIONS FOR INFINITE GRAPHS. Mark Pollicott University of Warwick

An introduction to arithmetic groups. Lizhen Ji CMS, Zhejiang University Hangzhou , China & Dept of Math, Univ of Michigan Ann Arbor, MI 48109

Math 110 Linear Algebra Midterm 2 Review October 28, 2017

Math 18, Linear Algebra, Lecture C00, Spring 2017 Review and Practice Problems for Final Exam

Zeta functions in Number Theory and Combinatorics

Counting Cycles in Strongly Connected Graphs

Maximum topological entropy of permutations and cycles. Deborah King. University of Melbourne. Australia

Linear Algebra Massoud Malek

Transcription:

Bowen-Lanford Zeta function, Math table talk Oliver Knill, 10/18/2016 Networks and Matrices Assume you have three friends who do not know each other. This defines a network containing 4 nodes in which you are the central node. One calls this network also a graph. In this case it has 4 vertices and 3 edges. A graph can be encoded by its adjacency matrix A. The encoding is to put A ij = 1, if person i is connected to person j. We do not connect a person to itself so that the matrix has zeros in the diagonal. A = 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 You see that the rows appear several times. The matrix has zero determinant. It also follows from the fact that there is no closed loop going through all this network visiting each person exactly once and passing from one to the other if they are friends. In other words, in the Leibniz sum of the determinant, there is not even one single term. Note that one could also set up a network, where friendship relations can go also one way. This is called a directed graph or digraph. We deal here with undirected graphs and call them finite simple graphs.. Here is an other network, representing a network of three friends: Its adjacency matrix is A(G) = 0 1 1 1 0 1 1 1 0.

2 Its determinant is 2. There are two patterns, cyclic paths through the network visiting each node once. Why are determinants important? You might have seen that they provide explicit formulas for solutions of systems of linear equations or that they are used to define eigenvalues as the roots of the characteristic polynomial. But first of all, they came historically decades before matrices were defined. Indeed, the word matrix comes from mater which is latin for mother. The mother of what? They are the mother of determinants since they produce determinants! The determinant of a matrix A is a natural function on the space of matrices. One main reason is the Cauchy-Binet multiplication formula det(ab) = det(a)det(a), found in 1812 and which shows that det is compatible with multiplication. It is curious for example that in 1812, when the product formula was found independently by Cauchy and Binet, there were no matrices around yet. Cliques and Connection Graphs A subgraph of a graph G is called a clique or simplex or complete sub graph if each vertex is connected to each other in that graph. This corresponds to a group of people in which each one knows each other. A single person is a clique, but also a group of two friends as well as a group of three friends. We can now look at the individual cliques as vertices of a new graph and connect two different cliques if they have a mutual common friend. This new graph is called the connection graph of G. Here is the connection graph of the star graph: Its adjacency matrix is A = 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 0 1 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0. It has determinant 0. You can see this by looking at the zero 0 0 block. And here is the connection graph G of the triangle G = K 3. It also has 7 vertices.

3 Its adjacency matrix of G is A = 0 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 0 It has determinant 6. The Leibniz definition of the determinant means here to sum over the 90 of the 7!=5040 nonzero possible patterns A 1,π(1) A 7,π(7) and adding up their signatures. The result is det(a) = 6. Of course, in linear algebra we learn to do that more effectively using row reduction. For combinatorics, it is useful to see determinants as a sum over permutations. Now lets look at the Fredholm matrices. For the connection graph G of the star graph G, it is 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 + A(G ) = 1 1 1 1 0 0 0. 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 Its determinant is 1. For the connection graph G of the triangle graph G, the Fredholm matrix is 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 + A(G ) = 1 0 0 1 0 1 1. 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 Its determinant is again 1. Coincidence? If you see coincidences like that, it is good to have a computer and try many different cases. In February, 2016, I noticed first that the Fredholm determinant of connection graphs always either have determinant 1 or 1. This is surprising as it implies that the inverse of the Fredholm matrix of a connection graph always has integer entries. Lets look at that next. Unimodular Matrices.

4 The space of n n integer matrices is also called M(n, Z). It contains GL(n, Z), the general linear group above the integers. The inverse of an integer n n matrix is not an integer matrix any more in general since the entries can become fractions. Lets look at the 2 2 case, where a matrix [ ] a b A = c d has the inverse [ ] d b A 1 = /(ad bc) c a We see here that the determinant matters. Indeed, there is a general Laplace formula for the inverse of a matrix which involves determinants of minors. The entry of A 1 ij det(a) is ( 1)i+j times the minor M ji (the determinant of the matrix where row j and column i has been deleted). We see that if det(a) = ±1, then the inverse matrix has integer entries. Related is Cramer s rule, found by the Swiss mathematician Gabriel Cramer (1704-1752). It is an explicit formula for x if A x = b. The Laplace formula and Cramer rules are equivalent since x = A 1 b and knowing solutions A x = e j gives columns x of A 1. Gabriel Cramer and Pierre-Simon Laplace The space of unimodular matrices GL(n, Z) however form a group. And we can state a theorem! Here it is: The Fredholm matrix of the adjacency matrix of a connection graph is always unimodular. A question is now of course, why this is so and what it means. The proof is done but is quite complicated still but hopefull appears in the next couple of weeks. We have two type of graphs now, one class for which the connection Fredholm determinant is equal to 1 and an other class, for which the connection Fredholm determinant is equal to 1. Lets find out how to characterize the classes. To do so, we experimented a lot more and correlated with other properties of graphs. After some search, (of course with the assistance of a computer) the following appeared (actually only in September 2016): The Fredholm determinant of a connection graph G is 1 if there are an even number of cliques in G with even number of members and 1 if there is an odd number of cliques in G with an even number of members. In other words, it is the cardinality of the number of odd dimensional cliques in G which matters. This implies that the log of the determinant is a valuation. The value for the union is the sum of the values of the individual graphs minus the value of the intersection. Some Genealogy The name Fredholm determinant was used by John Archibald Wheeler (1911-2008) in physics to determine scattering and cross sections [10]. Wheeler is given credit for spreading the name

5 black hole in a lecture of 1967 (but it was used by a journalist Ann Ewing (1921-2010) in an article Black Holes in space earlier in 1964 [2].) Wheeler is the doctor father of Richard Feynman and Arthur Wightman who in turn was the doctor father of my doctor father Oscar Lanford as well as the doctor father of my postdoc mentors Barry Simon at Caltech and Raphael de la Llave at UT. By the way, the book Trace ideals of Simon [7] is the best reference about Fredholm determinants. John Wheeler and Ann Ewing Bowen-Lanford Zeta function The Bowen-Lanford Zeta function of a shift transformation T on the set of all sequences on a finite alphabet V is defined as ζ(z) = exp( z n N n /n), n=1 where N n is the number of fixed points of T n. [1] The function therefore encodes the number of periodic points of the transformation. One can extend this in various way like looking at smooth maps on a compact manifold. Far reaching generalizations were given by Artin-Mazur and Ruelle. One is often interested in closed invariant subsets of T obtained by restricting the possible words. This can be encoded by a finite graph. An edge (a, b) in that graph tells that the transition a b and b a is possible. Using N n = tr(a n ) we see that that n=1 zn N n /n = tr(log(1 za)) = log(det(1 za) and ζ(z) = 1/ det(1 za). It is more exciting! If we look at the set of all possible strings as a space and the shift T as a dynamical system on this space, then we can look at prime orbits. For example, the sequence...[312312][312312]... is 6 periodic, but it is not prime as it is also...[312][312][312]... If we sum over all possible prime orbits x of T and denote by x its length, then ζ(z) = x prime (1 z x ) 1. With a change of variables p = e x and s = log(z) we can write this as an Ihara type zeta function (1 p(x) s ) 1 x prime which looks a lot now like the Riemann zeta function p (1 p s ) 1. See [4, 6]. The prime number theorem telling that the number of primes x is about x/ log(x) has an analogue in graph theory: the number of prime orbits x is about λ n 1/n where λ 1 is the largest Perron- Frobenius eigenvalue. Pollicott calls this the Prime Graph Theorem. By the way, there is a whole zoo of zeta functions. Several dozen of them [9]. See also the book [8].

6 Rufus Bowen and Oscar Lanford Fredholm Determinants Audrey Terras and Mark Pollicott The Fredholm determinant of a matrix A is defined as det(1 + A), where 1 is the identity matrix and det denotes the usual determinant. Why are Fredholm determinants important? Lets look at a graph G and its adjacency matrix A. By definition, the determinant det(a) is a sum over all cyclic permutations of the vertex set of V which can occur. Now, if we look at det(1 + A), then this allows for orbits to stay at some vertex. In other words, instead of all cyclic permutations, we look at all permutations of the vertex set of V. Forests and Trees Erik Fredholm and Barry Simon

7 Here is an other illustration what changes when we move from a matrix A to a matrix 1 + A. The Chebotarev-Shamis forest theorem [5] tells that det(1 + L) is the number of rooted forests in a graph G if L = B A is the Laplacian. Here B is the vertex degree diagonal matrix. The pseudo determinant det(l) however counts the number of rooted spanning trees. This is the famous Kirchhoff matrix tree theorem. The forest theorem follows immediately from a generalized Cauchy-Binet theorem [3], telling that det(1 + F T G) = P det(f P )det(g P ) which holds for any pair F, G of n m matrices and were the right hand side is a dot product of the minor giving all possible minors of the matrix, defined by the index set P which can be empty in which case det(a P ) = 1. Similarly, the Fredholm determinant det(1 + A) for the adjacency matrix A can be written as det(1 + A) = P det(a P )det(1 P ) which implies det(1 + A) = k=0 tr(λk (A)), where Λ k (A) is the k th exterior power of A. This can now be defined if A is a trace class because Λ k (A) 1 A k 1 /k! where 1 = tr( A ) is the trace class norm. This implies det(1 + A) e A 1. This allows to extend the notion of determinant to compact operators which are trace class. Open Problems Pavel Chebotarev and Elena Shamis We believe that both random variables G det(1 + A(G)) as well as G det(1 + A(G)) have a continuous limiting distribution on random graphs when suitably normalized: here are the distributions obtained by computing it for one million graphs with 50 nodes and edge probability p = 1/2: What is the structure of the inverse matrices of 1 + A(G ), where A(G ) is the connection graph of the connection graph G of G?

8 Here are some measurements. For many graphs, including the complete graph, wheel graphs, cross polytopes or cycle graphs, the inverse of the matrix 1 + A(G ) only takes the values 1, 0, 1. For star graphs with n rays, we see that the values of the inverse of 1+A(G) is (n 1), 1, 0, 1. For the utility graph, or the Petersen graph, the values are 2, 1, 0, 1. Among the 13 Archimedian solids, we see 6 for which the value takes values in { 2, 1, 0, 1} and 7 where the value takes value in { 1, 0, 1}. On the 13 Catalan solids, the cases { 1, 0, 1}, { 3, 2, 1, 0, 1}, { 4, 2, 1, 1, 0, 1} and { 4, 3, 2, 1, 0, 1} occur. We see that the values which the inverse matrix of 1+A(G ) is the same for G and the Barycentric refinement of G which has the same vertex set like G but for which two cliques are connected if one is contained in the other. We have no explanation for this yet. This would for example that the minimal value of (1 + A(G) ) 1 ij is a topological invariant of G and could be used to get topological invariants of continuum spaces like manifolds or varieties. Here we see the Fredholm matrix of a random connection graph. Most of its entries of (1+A(G) ) 1 ij are 0, 1, 1, but there are a few entries different from 0, 1, 1. A connection Fredholm matrix and its inverse References [1] R. Bowen and O. E. Lanford, III. Zeta functions of restrictions of the shift transformation. In Global Analysis (Proc. Sympos. Pure Math., Vol. XIV, Berkeley, Calif., 1968), pages 43 49. Amer. Math. Soc., 1970. [2] E. Brown. Ann Ewing dies; science journalist turned nation s eyes to black holes. Washington Post, August 1, 2010. [3] O. Knill. A Cauchy-Binet theorem for Pseudo determinants. Linear Algebra and its Applications, 459:522 547, 2014. [4] M. Pollicott. Dynamical Zeta functions. Warwick, 2015. [5] E.V. Shamis P.Yu, Chebotarev. A matrix forest theorem and the measurement of relations in small social groups. Avtomat. i Telemekh., 9:125 137, 1997. [6] D. Ruelle. Dynamical Zeta Functions for Piecewise Monotone Maps of the Interval. CRM Monograph Series. AMS, 1991. [7] B. Simon. Trace Ideals and their Applications. AMS, 2. edition, 2010. [8] A. Terras. Zeta functions of Graphs, volume 128 of Cambridge studies in advanced mathematics. Cambridge University Press, 2011. [9] M.R. Watkins. List of known zeta functions. http://empslocal.ex.ac.uk/people/staff/mrwatkin//zeta/directoryofzetafunctions.htm, Retrieved: October 16, 2016. [10] J. Wheeler. On the mathematical description of light nuclei by the method of resonating group structure. Physical Review, 52:1107, 1937.