An Introduction to Algebraic Graph Theorybuzzard.ups.edu/talks/beezer-2009-reed-agt.pdfAn...
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An Introduction to Algebraic Graph Theory
Department of Mathematics and Computer ScienceUniversity of Puget Sound
Mathematics Department SeminarReed College
October 1, 2009
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Labeling Puzzles
Assign a single real numbervalue to each circle.
For each circle, sum thevalues of adjacent circles.
Goal:Sum at each circle should bea common multiple of thevalue at the circle.
−1 1
0 0
1 −1
Common multiple: −2
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Example Solutions
1
1 1
1
11
Common multiple: 3
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Example Solutions
1
1 −1
−1
−11
Common multiple: 1
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Example Solutions
0
−1 −1
0
11
Common multiple: 0
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Example Solutions
1
0 −1
0
Common multiple: −1
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Example Solutions
−1 0
10
Common multiple: 0
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Example Solutions
4
4
r=Sqrt(17)
r−1
r−1
Common multiple: 12 (r + 1) = 1
2
(√17 + 1
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Graphs
A graph is a collection of vertices (nodes, dots) wheresome pairs are joined by edges (arcs, lines).
The geometry of the vertex placement, or the contours of the edges areirrelevant. The relationships between vertices are important.
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Adjacency Matrix
Given a graph, build a matrix of zeros and ones as follows:Label rows and columns with vertices, in the same order.Put a 1 in an entry if the corresponding vertices are connected by an edge.Otherwise put a 0 in the entry.
x w
vu
u v w x
u 0 1 0 1v 1 0 1 1w 0 1 0 1x 1 1 1 0
Always a symmetric matrix with zerodiagonal.
Useful for computer representations.
Entree to linear algebra, especiallyeigenvalues and eigenvectors.
Symmetry groups of graphs is the otherbranch of Algebraic Graph Theory.
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Adjacency Matrix
Given a graph, build a matrix of zeros and ones as follows:Label rows and columns with vertices, in the same order.Put a 1 in an entry if the corresponding vertices are connected by an edge.Otherwise put a 0 in the entry.
x w
vu
u v w x
u 0 1 0 1v 1 0 1 1w 0 1 0 1x 1 1 1 0
Always a symmetric matrix with zerodiagonal.
Useful for computer representations.
Entree to linear algebra, especiallyeigenvalues and eigenvectors.
Symmetry groups of graphs is the otherbranch of Algebraic Graph Theory.
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Adjacency Matrix
Given a graph, build a matrix of zeros and ones as follows:Label rows and columns with vertices, in the same order.Put a 1 in an entry if the corresponding vertices are connected by an edge.Otherwise put a 0 in the entry.
x w
vu
u v w x
u 0 1 0 1v 1 0 1 1w 0 1 0 1x 1 1 1 0
Always a symmetric matrix with zerodiagonal.
Useful for computer representations.
Entree to linear algebra, especiallyeigenvalues and eigenvectors.
Symmetry groups of graphs is the otherbranch of Algebraic Graph Theory.
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Eigenvalues of Graphs
λ is an eigenvalue of a graph⇔ λ is an eigenvalue of the adjacency matrix⇔ A~x = λ~x for some vector ~x
Adjacency matrix is real, symmetric ⇒real eigenvalues, algebraic and geometric multiplicities are equalminimal polynomial is product of linear factors for distinct eigenvalues
Eigenvalues:
λ = 3 m = 1
λ = 1 m = 5
λ = −2 m = 4
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Eigenvalues of Graphs
λ is an eigenvalue of a graph⇔ λ is an eigenvalue of the adjacency matrix⇔ A~x = λ~x for some vector ~x
Adjacency matrix is real, symmetric ⇒real eigenvalues, algebraic and geometric multiplicities are equalminimal polynomial is product of linear factors for distinct eigenvalues
Eigenvalues:
λ = 3 m = 1
λ = 1 m = 5
λ = −2 m = 4
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Eigenvalues of Graphs
λ is an eigenvalue of a graph⇔ λ is an eigenvalue of the adjacency matrix⇔ A~x = λ~x for some vector ~x
Adjacency matrix is real, symmetric ⇒real eigenvalues, algebraic and geometric multiplicities are equalminimal polynomial is product of linear factors for distinct eigenvalues
Eigenvalues:
λ = 3 m = 1
λ = 1 m = 5
λ = −2 m = 4
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4-Dimensional Cube
Vertices: Length 4 binary strings
Join strings differing in exactly onebit
Generalizes 3-D cube
0001
0010
0100
1000
0111
1011
1101
1110
0000 1111
1010
0101
0011
1100
Eigenvalues:
λ = 4 λ = 2 λ = 0 λ = −2 λ = −4
m = 1 m = 4 m = 6 m = 4 m = 1
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Regular Graphs
A graph is regular if every vertex has the same number of edges incident.The degree is the common number of incident edges.
Theorem
Suppose G is a regular graph of degree r . Then
r is an eigenvalue of G
The multiplicity of r is the number of connected components of G
Regular of degree 3with 2 componentsimplies that λ = 3will be an eigenvalue ofmultiplicity 2.
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Proof.
Let ~u be the vector where every entry is 1. Then
A~u = A
11...1
=
rr...r
= r~u
For each component of the graph, form a vector with 1’s in entriescorresponding to the vertices of the component, and zeros elsewhere.
These eigenvectors form a basis for the eigenspace of r .(Their sum is the vector ~u above.)
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Labeling Puzzles Explained
The product of a graph’s adjacency matrix with a column vector, A~u,forms sums of entries of ~u for all adjacent vertices.
If ~u is an eigenvector, then these sums should equal a common multiple ofthe numbers assigned to each vertex. This multiple is the eigenvalue.
So the puzzles earlier were simply asking for:
eigenvectors (assignments of numbers to vertices), and
eigenvalues (common multiples)
of the adjacency matrix of the graph.
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Eigenvalues and Eigenvectors of the Prism
6 5
32
1
4
A =
0 1 0 1 0 11 0 1 0 0 10 1 0 1 1 01 0 1 0 1 00 0 1 1 0 11 1 0 0 1 0
λ = 3 λ = 1 λ = 0 λ = −2
111111
11−1−1−11
0−1−1011
,
1−1−1100
0−110−11
,
−11−1100
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Eigenvalues and Eigenvectors
1 2
3 4
A =
0 1 0 11 0 1 10 1 0 11 1 1 0
λ = −1 λ = 0 λ =1
2
(√17 + 1
)λ =
1
2
(−√
17 + 1)
0−101
−1010
√
17− 14√
17− 14
√
17 + 1−4√
17 + 1−4
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Powers of Adjacency Matrices
Theorem
Suppose A is the adjacency matrix of a graph. Then the number of walksof length ` between vertices vi and vj is the entry in row i, column j of A`.
Proof.
Base case: ` = 1. Adjacency matrix describes walks of length 1.[A`+1
]ij
=[A`A
]ij
=n∑
k=1
[A`]ik
[A]kj =∑
k:vk adj vj
[A`]ik
= total ways to walk in ` steps from vi to vk , a neighbor of vj
i
jk
k
l
l
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Powers of Adjacency Matrices
Theorem
Suppose A is the adjacency matrix of a graph. Then the number of walksof length ` between vertices vi and vj is the entry in row i, column j of A`.
Proof.
Base case: ` = 1. Adjacency matrix describes walks of length 1.[A`+1
]ij
=[A`A
]ij
=n∑
k=1
[A`]ik
[A]kj =∑
k:vk adj vj
[A`]ik
= total ways to walk in ` steps from vi to vk , a neighbor of vj
i
jk
k
l
l
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Adjacency Matrix Power
Number of walks of length 3 between vertices 1 and 3?
A =
0 1 0 11 0 1 10 1 0 11 1 1 0
A3 =
2 5 2 55 4 5 52 5 2 55 5 5 4
4
1 2
3
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Characteristic Polynomial
Recall the characteristic polynomial of a square matrix A is det(λI − A).Roots of this polynomial are the eigenvalues of the matrix.
For the adjacency matrix of a graph on n vertices:
Coefficient of λn is 1Coefficient of λn−1 is zero (trace of adjacency matrix)Coefficient of −λn−2 is number of edgesCoefficient of −2λn−3 is number of triangles
Characteristic polynomialλ4 − 5λ2 − 4λ+ · · ·
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Diameter and Eigenvalues
The diameter of a graph is the longest shortest path.In other words, find the shortest route between each pair of vertices, thenask which pair is farthest apart?
Theorem
Suppose G is a graph of diameter d.Then G has at least d + 1 distinct eigenvalues.
Proof.
Minimal polynomial is product of linear factors, one per distinct eigenvalue.
There are zero short walks between vertices that are far apart.
So the first d powers of A are linearly independent.
So A cannot satisfy a polynomial of degree d or less.
Thus minimal polynomial has at least d + 1 factorshence at least d + 1 eigenvalues.
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Minimal Polynomial for a Diameter 3 Graph
1
2
3
4
5
6
Distinct Eigenvalues: λ = 2, 1, −1, −2
Minimal Polynomial:(x−2)(x−1)(x−(−1))(x−(−2)) = x4−5x2+4
Check that A4 − 5A2 + 4 = 0
A A2 A30 1 0 0 0 11 0 1 0 0 00 1 0 1 0 00 0 1 0 1 00 0 0 1 0 11 0 0 0 1 0
2 0 1 0 1 00 2 0 1 0 11 0 2 0 1 00 1 0 2 0 11 0 1 0 2 00 1 0 1 0 2
0 3 0 2 0 33 0 3 0 2 00 3 0 3 0 22 0 3 0 3 00 2 0 3 0 33 0 2 0 3 0
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Results Relating Diameters and Eigenvalues
Regular graph: n vertices, degree r , diameter dLet λ denote second largest eigenvalue (r is largest eigenvalue)
Alon, Milman, 1985
d ≤ 2
⌈(2r
r − λ
) 12
log2 n
⌉
Mohar, 1991
d ≤ 2
⌈(2r − λ
4 (r − λ)
)ln (n − 1)
⌉
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Results Relating Diameters and Eigenvalues
Regular graph: n vertices, degree r , diameter dLet λ denote second largest eigenvalue (r is largest eigenvalue)
Alon, Milman, 1985
d ≤ 2
⌈(2r
r − λ
) 12
log2 n
⌉
Mohar, 1991
d ≤ 2
⌈(2r − λ
4 (r − λ)
)ln (n − 1)
⌉
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Results Relating Diameters and Eigenvalues
Regular graph: n vertices, degree r , diameter dLet λ denote second largest eigenvalue (r is largest eigenvalue)
Alon, Milman, 1985
d ≤ 2
⌈(2r
r − λ
) 12
log2 n
⌉
Mohar, 1991
d ≤ 2
⌈(2r − λ
4 (r − λ)
)ln (n − 1)
⌉
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Bipartite Graphs
A graph is bipartite if the vertex set can be split into two parts, so thatevery edge goes from part to the other. Identical to being “two-colorable.”
Theorem
A graph is bipartite if and only ifthere are no cycles of odd length.
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Eigenvalues of Bipartite Graphs
Theorem
Suppose G is a bipartite graph with eigenvalue λ.Then −λ is also an eigenvalue of G.
λ = 3 λ = −3 λ = 1 λ = −1
11111111
1−11−11−11−1
100−1−1001
1001−100−1
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Proof.
Order the vertices according to the two parts. Then
A =
[0 BBt 0
]
For an eigenvector ~u =
[~u1
~u2
]of A for λ,
[λ~u1
λ~u2
]= λ
[~u1
~u2
]= λ~u = A~u =
[0 BBt 0
] [~u1
~u2
]=
[B~u2
Bt~u1
]
Now set ~v =
[−~u1
~u2
]and compute,
A~v =
[0 BBt 0
] [−~u1
~u2
]=
[B~u2
−Bt~u1
]=
[λ~u1
−λ~u2
]= −λ
[−~u1
~u2
]= −λ~v
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Bipartite Graphs on an Odd Number of Vertices
Theorem
Suppose G is a bipartite graph with an odd number of vertices.Then zero is an eigenvalue of the adjacency matrix of G .
Proof.
Pair each eigenvalue with its negative.At least one eigenvalue must then equal its negative.
λ =√
7 m = 1
λ = 1 m = 2
λ = 0 m = 1
λ = −1 m = 2
λ = −√
7 m = 1
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Application: Friendship Theorem
Theorem
Suppose that at a party every pair ofguests has exactly one friend in common.Then there are an odd number of guests,and one guest knows everybody.
Model party with a graph
Vertices are guests, edges are friends
Two possibilities:
1. Graph to the right2. Algebraic graph theory say theadjacency matrix has eigenvalueswith non-integer multiplicities
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Application: Friendship Theorem
Theorem
Suppose that at a party every pair ofguests has exactly one friend in common.Then there are an odd number of guests,and one guest knows everybody.
Model party with a graph
Vertices are guests, edges are friends
Two possibilities:1. Graph to the right2. Algebraic graph theory say theadjacency matrix has eigenvalueswith non-integer multiplicities
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Application: BuckministerfullereneMolecule composed solely of 60carbon atoms
“Carbon-60,” “buckyball”
At each atom two single bonds andone double bond describe a graphthat is regular of degree 3
Graph is the skeleton of a truncatedicosahedron, a soccer ball
60× 60 adjacency matrix
Eigenvalues of adjacency matrixpredict peaks in mass spectrometry
First created in 1985(1996 Nobel Prize in Chemistry)
Illustration by Michael Strock
Wikipedia, GFDL License
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Sage and Graphs
Sage is software for mathematics, “creating a viable free open sourcealternative to Magma, Maple, Mathematica and Matlab.”
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Sage and Graph Eigenvalues
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Isospectral, Non-Isomorphic Circulant Graphs
A graph is circulant if each row of the adjacency matrixis a shift (by one) of the prior row.
Julia Brown (nee Lazenby), 2008 Reed thesis
Infinite family of pairs of digraphs:identical spectra, but non-isomorphic graphs
Vertices number n = 2rp, with r ≥ 2, p prime
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Isospectral, Non-Isomorphic Circulant Graphs
A graph is circulant if each row of the adjacency matrixis a shift (by one) of the prior row.
Julia Brown (nee Lazenby), 2008 Reed thesis
Infinite family of pairs of digraphs:identical spectra, but non-isomorphic graphs
Vertices number n = 2rp, with r ≥ 2, p prime
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n = 22 · 7
Sage: 6 lines to construct, 2 lines to plot, 2 lines to verifyAlso verified for n = 25 · 13 = 416 (in under 6 seconds)
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Moral of the Story
Graph properties predict linear algebra properties(regular of degree r ⇒ r is an eigenvalue)
Linear algebra properties predict graph properties(functions of two largest eigenvalues bound the diameter)
Do eigenvalues characterize graphs? No.
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Moral of the Story
Graph properties predict linear algebra properties(regular of degree r ⇒ r is an eigenvalue)
Linear algebra properties predict graph properties(functions of two largest eigenvalues bound the diameter)
Do eigenvalues characterize graphs? No.
Rob Beezer (U Puget Sound) An Introduction to Algebraic Graph Theory Reed Math Oct 1 2009 34 / 35
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Moral of the Story
Graph properties predict linear algebra properties(regular of degree r ⇒ r is an eigenvalue)
Linear algebra properties predict graph properties(functions of two largest eigenvalues bound the diameter)
Do eigenvalues characterize graphs? No.
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Bibliography
Bibliography
Norman Biggs, Algebraic Graph Theory, Second EditionCambridge Mathematical Library, 1974, 1993
Chris Godsil, Gordon Royle, Algebraic Graph TheorySpringer, 2001
Julia Brown, Isomorphic and Nonisomorphic, Isospectral CirculantGraphs, arXiv:0904.1968v1, 2009
Robert Beezer, A First Course in Linear Algebrahttp://linear.ups.edu, GFDL License
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