Friedman’s theorem: a new proof using strong convergence

1 Graphs: introduction and basic spectral theory

In this chapter, we introduce basic definitions and properties related to (multi)graphs and their spectrum. We refer the reader to [ for a more detailed introduction and motivations.

As in Graph, a graph \(G\) is a set of vertices \(V(G)\), and a set of edges \(E(G)\), such that each edge links two unordered, possibly identical vertices. Note that this definition allows for loops (i.e. edges going from one vertex to itself) and multi-edges (i.e. multiple edges connecting two vertices). This convention is essential to the proof we are aiming for, which is why we use Graph instead of the more developed SimpleGraph.

1.1 Basic graph theory

We here introduce a few basic notions which will be useful to the project. These definitions should probably be added to Mathlib and hence written carefully, with good API and following the naming conventions, in particular of SimpleGraph.

1.1.1 Darts

We define a notion of darts, which are oriented edges on the graph. As opposed to the case of SimpleGraph, I could not think of a natural explicit description of darts. However they are defined by the following structure.

Definition 1
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We define a set \(D(G)\) of darts on \(G\) with applications

  • \(\mathrm{start}, \mathrm{end}: D(G) \rightarrow V(G)\);

  • \(\mathrm{edge}: D(G) \rightarrow E(G)\);

  • \(\mathrm{symm}: D(G) \rightarrow D(G)\);

such that:

  • for all \(d \in D(G)\), \(\mathrm{edge}(d)\) links \(\mathrm{start}(d)\) and \(\mathrm{end}(d)\);

  • \(\mathrm{edge}\circ \mathrm{symm}= \mathrm{edge}\);

  • \(\mathrm{start}\circ \mathrm{symm}= \mathrm{end}\) and \(\mathrm{end}\circ \mathrm{symm}= \mathrm{start}\);

  • for \(d \in D(G)\), \(\mathrm{start}(d) = \mathrm{end}(d)\) if and only if \(\mathrm{edge}(d)\) is a loop;

  • \(\mathrm{edge}\) is surjective;

  • for \(d, d' \in D(G)\), \(\mathrm{edge}(d) = \mathrm{edge}(d')\) iff \(d=d'\) or \(d=\mathrm{symm}(d')\);

  • for all \(d \in D(G)\), \(\mathrm{symm}(d) \neq d\).

Lemma 2

For any \(d \in D(G)\), \(\mathrm{symm}(\mathrm{symm}(d))=d\).

Proof

We have \(\mathrm{edge}(\mathrm{symm}(\mathrm{symm}(d))) = \mathrm{edge}(\mathrm{symm}(d)) = \mathrm{edge}(d)\) so have \(\mathrm{symm}(\mathrm{symm}(d))=d\) or \(\mathrm{symm}(\mathrm{symm}(d))=\mathrm{symm}(d)\). The latter is impossible due to \(\mathrm{symm}\) having no fixed point.

Definition 3

Let \(x \in V(G)\). The dartset of \(x\) is the set

\begin{equation} \mathrm{dartSet}(x) := \{ d \in D(G) \, : \, \mathrm{start}(d)=x\} . \end{equation}
1

Definition 4

Let \(x\), \(y \in V(G)\). We define the dartset of \((x,y)\) as

\begin{equation*} \mathrm{dartSet}(x,y) = \{ d \in D(G) : \mathrm{start}(d) = x, \mathrm{end}(d) = y\} . \end{equation*}

Let \(x, y \in V(G)\). The following are equivalent:

  1. \(x\) and \(y\) are adjacent;

  2. there exists \(d \in \mathrm{dartSet}(x)\) such that \(\mathrm{end}(d)=y\);

  3. \(\mathrm{dartSet}(x,y)\) is not empty.

Proof

We start by proving (1) \(\Rightarrow \) (2). Assume \(x\) and \(y\) are adjacent. Then there exists an edge \(e\) linking \(x\) and \(y\). The function \(\mathrm{edge}\) is surjective so there exists \(d \in D(G)\) such that \(\mathrm{edge}(d)=e\). \(e\) links \(x\) and \(y\) but also \(\mathrm{start}(d)\) and \(\mathrm{end}(d)\) so:

  • either \(x=\mathrm{start}(d)\) and \(y=\mathrm{end}(d)\), in which case \(d \in \mathrm{dartSet}(x)\) satisfies the claim;

  • otherwise \(x = \mathrm{end}(d) = \mathrm{start}(\mathrm{symm}(d))\) and \(y = \mathrm{start}(d) = \mathrm{end}(\mathrm{symm}(d))\) and hence \(\mathrm{symm}(d) \in \mathrm{dartSet}(x)\) satisfies the claim.

The implication (2) \(\Rightarrow \) (3) is immediate by definition of \(\mathrm{dartSet}(x,y)\).

To conclude, (3) \(\Rightarrow \) (1) because, if \(d \in \mathrm{dartSet}(x,y)\), then \(\mathrm{edge}(d)\) links \(x\) and \(y\) and hence they are adjacent.

Lemma 6

For any \(e \in E(G)\), there exists \(d \in D(G)\) such that \(\mathrm{edge}^{-1}(\{ e\} ) = \{ d, \mathrm{symm}(d)\} \).

Proof

Let \(e \in E(G)\). The map \(\mathrm{edge}\) is surjective so there exists \(d \in D(G)\) such that \(\mathrm{edge}(d)=e\). We have \(\mathrm{edge}(\mathrm{symm}(d))=\mathrm{edge}(d)=e\). This proves \(\{ d, \mathrm{symm}(d)\} \subseteq \mathrm{edge}^{-1}(\{ e\} )\). For the other direction, if \(d' \in D(G)\) satisfies \(\mathrm{edge}(d')=e\), then \(\mathrm{edge}(d)=\mathrm{edge}(d')\) which implies by definition that \(d'=d\) or \(d'=\mathrm{symm}(d)\), which is the claim.

Lemma 7

Let \(x,y \in V(G)\). The range of the restriction of the function \(\mathrm{edge}\) to \(\mathrm{dartSet}(x,y)\) is exactly the set \(\{ e \in E(G) : e \text{ links } x, y\} \). Furthermore, this map is \(1\)-to-\(1\) if \(x \neq y\) and \(2\)-to-\(1\) if \(x=y\).

Proof

Let \(d \in \mathrm{dartSet}(x,y)\). Then, \(\mathrm{edge}(d) \in E(G)\) links \(\mathrm{start}(d)=x\) and \(\mathrm{end}(d)=y\). So \(\mathrm{edge}(d)\) belongs in the claimed set.

Now take \(e \in E(G)\) linking \(x\) and \(y\). By Lemma 6, there exists \(d \in D(G)\) such that \(\mathrm{edge}^{-1}(\{ e\} ) = \{ d, \mathrm{symm}(d)\} \). We check that \(d\) or \(\mathrm{symm}(d)\) belongs in \(\mathrm{dartSet}(x,y)\), and that both of them do iff \(x=y\), which leads to the claim.

We can alternatively define a graph from its darts in the following fashion.

Definition 8
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Let \(V\) be a set. A dart-like structure on \(V\) is the data of a set of darts \(D\), together with maps \(\mathrm{start}, \mathrm{end}: D \rightarrow V\) and \(\mathrm{symm}: D \rightarrow D\) satisfying the following:

  • \(\mathrm{start}\circ \mathrm{symm}= \mathrm{end}\) and \(\mathrm{end}\circ \mathrm{symm}= \mathrm{start}\);

  • \(\mathrm{symm}\) is an involution with no fixed point.

Definition 9

Let \(V\) be a set and \(D\), \(\mathrm{start}, \mathrm{end}, \mathrm{symm}\) be a dart-like structure. We define a relation on \(D\) by:

\begin{equation*} \forall d, d’ \in D, \quad d \sim d’ \Leftrightarrow (d=d’ \text{ or } d = \mathrm{symm}(d’)). \end{equation*}
Lemma 10

The relation \(\sim \) on \(D\) is an equivalence relation, and all of its equivalence classes are of the form \(\{ d, \mathrm{symm}(d)\} \) (the two of which are distinct).

Proof

This is a classic fact for involutions with no fixed points.

Definition 11

Let \(V\) be a set and \(D\), \(\mathrm{start}, \mathrm{end}, \mathrm{symm}\) be a dart-like structure. We define a graph \(G\) associated to this structure by taking:

  • \(V(G) := V\) as the vertex set;

  • \(E(G) := D(G) \diagup \sim \) as the edge set;

  • for \(e \in E(G)\), if \(d \in D(G)\) is a representant of \(e\), then \(e\) links \(\mathrm{start}(d)\) and \(\mathrm{end}(d)\).

Proof

The only thing to check is that the definition of the links does not depend on the choice of a representative, which is true since \(\mathrm{start}\circ \mathrm{symm}= \mathrm{end}\) and \(\mathrm{end}\circ \mathrm{symm}= \mathrm{start}\).

Theorem 12

The graph constructed in Definition 11 is a graph, and \(D(G) := D\), \(\mathrm{start}\), \(\mathrm{end}\), \(\mathrm{symm}\) are darts of this graph, with the projection edge map \(\mathrm{edge}: D(G) \rightarrow E(G)\).

1.1.2 Subgraphs

The definition of subgraph is easier for Graph as the vertexset is just a subset.

Definition 13
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A subgraph \(H\) of \(G\) is a graph (ofthe same type as \(G\)) defined by:

  • a vertex set \(V(H)\) included in the vextex set of \(G\)

  • an edge set \(E(H)\) included in the edge set of \(H\)

  • the same \(\mathrm{IsLink}\) relation, i.e. if \(e\) links \(x\) and \(y\) in \(H\) then it also links \(x\) and \(y\) in \(G\).

Lemma 14

Let \(H\) be a subgraph of \(G\). For any \(x\), \(y\) in \(V(H)\), if \(x\) and \(y\) are adjacent for \(H\), then they are adjacent for \(G\).

Definition 15

We define the union, resp. intersection of two subgraphs by taking the union of their respective vertex sets and edge sets.

Definition 16

A subgraph \(H\) of \(G\) is induced if, for any \(x, y \in V(H)\), for any \(e \in E(G)\), if \(e\) links \(x\) and \(y\) in \(G\), then \(e \in E(H)\).

Definition 17

Let \(U\) be a subset of \(V(G)\). Then we define a subgraph \(H\) induced by \(U\) by letting:

  • \(V(H) := U\);

  • \(E(H) := \{ e \in E(G) : \text{ both endpoints of }e \text{ lie in } U\} \).

Lemma 18

A subgraph is induced iff it is induced from as subset of \(V(G)\).

1.1.3 Walks and reachability

We define a notion of walk on the graph.

Definition 19
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Let \(x, y \in V(G)\). A walk from \(x\) to \(y\) on \(G\) is given by two finite lists:

  • a list \((v_i)_{0 \leq i \leq m}\) of vertices in \(V(G)\);

  • a list \((e_i)_{1 \leq i \leq m}\) of edges in \(E(G)\);

satisfying the following:

  • \(v_0=x\) and \(v_m=y\);

  • for any \(i \in \{ 1, \ldots , m\} \), \(e_i\) links \(v_{i-1}\) and \(v_i\).

Definition 20
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The length of a walk is its number of edges.

The following definition is actually what we want if we want to count walks on the multi-graph, I am not sure if it would be accepted as a definition though?

Definition 21
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Let \(x, y \in V(G)\). A dartwalk from \(x\) to \(y\) on \(G\) is a finite list of darts \((d_i)_{1 \leq i \leq m}\) where:

  • \(\mathrm{start}(d_1)=x\);

  • \(\mathrm{end}(d_m)=y\);

  • for all \(1 \leq i {\lt} m\), \(\mathrm{end}(d_i)=\mathrm{start}(d_{i+1})\).

Definition 22

The support of the dartwalk \((d_i)_{1 \leq i \leq m}\) is the list \((x_i)_{0 \leq i \leq m}\) with \(x_0=\mathrm{start}(d_1)\) and \(x_i = \mathrm{end}(d_i)\) for \(1 \leq i \leq m\).

Definition 23
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We define a binary relation on \(V(G)\) by: \(x \sim y\) if there exists a walk from \(x\) to \(y\) on \(G\). We then say \(x\) and \(y\) are reachable.

Proposition 24
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The reachability relation is an equivalence relation on \(V(G)\).

Proof

The reachability relation is reflexive: \(x \sim x\) for any \(x \in V(G)\). Indeed, let \(x \in V(G)\). We take \(m=0\) and set \(v_0=x\). Then this is a walk from \(x\) to \(x\) on \(G\).

The reachability relation is symmetric: if \(x \sim y\) then \(y \sim x\). Let \(x\), \(y\) be two vertices. We assume \(x \sim y\). Let \((v_i)_{0 \leq i \leq m}\) and \((e_i)_{1 \leq i \leq m}\) be a walk from \(x\) to \(y\). Then \((v_{m-i})_{0 \leq i \leq m}\) and \((e_{m+1-i})_{1 \leq i \leq m}\) is a walk from \(y\) to \(x\). So \(y \sim x\).

The reachability relation is transitive: if \(x \sim y\) and \(y \sim z\) then \(x \sim z\). Assume \(x \sim y\) and \(y \sim z\). Let \((v_i)_{0 \leq i \leq m}\) and \((e_i)_{1 \leq i \leq m}\) be a walk from \(x\) to \(y\) and \((v_i')_{0 \leq i \leq m'}\), \((e_i')_{1 \leq i \leq m'}\) be a walk from \(y\) to \(z\). We let \(m''=m+m'\) and define for \(i \in \{ 0, \ldots , m'\} \)

\begin{equation} v_i'' := \begin{cases} v_i & \text{if } i \leq m \\ v_{i-m-1}’ & \text{otherwise} \end{cases} \end{equation}
2

and for \(i \in \{ 1, \ldots , m'\} \)

\begin{equation} e_i'' := \begin{cases} e_i & \text{if } i \leq m \\ e_{i-m}’ & \text{otherwise}. \end{cases} \end{equation}
5

Then this is a walk from \(x\) to \(z\), hence \(x \sim z\).

Lemma 25
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Let \(x\), \(y\) in \(V(G)\). If \(x\) and \(y\) are adjacent then \(x\) and \(y\) are reachable.

Proof

By definition of adjacency, there exists an edge \(e \in E(G)\) linking \(x\) and \(y\). Then the walk \((x, y)\) and \((e)\) is a walk from \(x\) to \(y\).

Definition 26

Let \(x, y \in V(G)\). A path from \(x\) to \(y\) on \(G\) is a walk \((v_i)_{0 \leq i \leq m}\), \((e_i)_{1 \leq i \leq m}\) such that, for all \(i \neq j\), \(v_i \neq v_j\).

Definition 27

A dartwalk \((d_i)_{1 \leq i \leq k}\) is a dartpath if all vertices in its support are distinct.

Definition 28
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The graph \(G\) is a tree if, for all \(x\), \(y \in V(G)\), there exists a unique dartpath from \(x\) to \(y\).

1.1.4 Connectedness

We define the notion of connectedness and the connected components of a graph.

Definition 29
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A graph is preconnected if any pair of vertices is reachable.

Definition 30
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The graph \(G\) is connected if it is preconnected and \(V(G)\) is not empty.

Lemma 31

The graph \(G\) is connected if and only if there exists \(x \in V(G)\) such that, for all \(y \in V(G)\), \(x\) and \(y\) are reachable.

Definition 32

A connected component of \(G\) is an equivalence class of \(V(G)\) for the relation reachable.

Definition 33

For any vertex \(x\), we define \(\mathrm{cc}(x)\) to be the unique connected component of \(G\) containing \(x\).

Lemma 34

Let \(x, y \in V(G)\). Then \(\mathrm{cc}(x) = \mathrm{cc}(y)\) iff \(x\) and \(y\) are reachable.

Let \(C\) be a connected component of \(G\). Then the subgraph induced by \(C\) is connected.

Definition 36

We denote as \(\mathrm{ConnComp}(G)\) the set of connected components of \(G\).

Proposition 37

\(\mathrm{ConnComp}(G)\) is a partition of \(V(G)\).

Lemma 38

If \(V(G)\) is finite, then \(\mathrm{ConnComp}(G)\) is finite and \(\# V(G) \leq \# \mathrm{ConnComp}(G)\).

1.1.5 Locally finite graph, degree and regularity

Definition 39

Let \(x\) be a vertex of \(G\). We say \(G\) is locally finite at \(x\) if \(\mathrm{dartSet}(x)\) is finite.

Definition 40

\(G\) is said to be locally finite if, for all \(x \in V(G)\), \(G\) is locally finite at \(x\).

Definition 41

Let \(x\) be a vertex of \(G\). We assume \(G\) is locally finite at \(x\). The degree of \(x\) is defined as \(\mathrm{deg}(x) = \# \mathrm{dartSet}(x)\).

Lemma 42
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If \(G\) is locally finite at \(x \in V(G)\), then \(\# \mathrm{dartSet}(x,x)\) is an even number.

Lemma 43

Let \(x \in V(G)\). \(G\) is locally finite at \(x\) iff \(\mathrm{incidenceSet}(x)\) is finite, and in this case,

\begin{equation*} \deg (x) = \# \mathrm{incidenceSet}(x) + \mathrm{loopSet}(x). \end{equation*}
Proof

Using Lemma 7,

\begin{align*} \deg (x) & = \sum _{y \in V(G)} \# \mathrm{dartSet}(x,y) \\ & = 2 \# \{ e \in E(G) : e \text{ links }x,x\} + \sum _{y \in V(G), y \neq x} \# \{ e \in E(G) : e \text{ links }x,y\} \\ & = 2 \# \mathrm{loopSet}(x) + (\# \mathrm{incidenceSet}(x) - \# \mathrm{loopSet}(x)) \end{align*}

which leads to the desired equality. Hence \(G\) is locally finite at \(x\) iff \(\mathrm{incidenceSet}(x)\) and \(\mathrm{loopSet}(x)\) are both finite, which is equivalent to \(\mathrm{incidenceSet}(x)\) being finite as \(\mathrm{loopSet}(x) \subseteq \mathrm{incidenceSet}(x)\).

Definition 44

Let \(\delta \) be a non-negative integer. We say \(G\) is regular of degree \(\delta \) if it is not empty, locally finite and, for every \(x \in V(G)\), \(\deg (x) = \delta \).

Lemma 45

The graph \(G\) is regular of degree \(0\) iff \(E(G)=\emptyset \).

Definition 46

We say \(G\) is locally bounded if it is locally finite and there exists a constant \(D\) such that, for all \(x \in V(G)\), \(\deg (x) \leq D\).

1.1.6 Bipartiteness

Definition 47
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The graph \(G\) is bipartite if there exists a partition \(U_1\), \(U_2\) of \(V(G)\) such that, for all \(e \in E(G)\), \(e\) links an element of \(U_1\) and an element of \(U_2\).

1.1.7 Metric

Definition 48
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Let \(x, y \in V(G)\). The extended distance \(\mathrm{edist}(x,y)\) between \(x\) and \(y\) is the length of the shortest walk from \(x\) to \(y\) (with value in \(\mathbb {N}\cup \{ \infty \} \)).

Lemma 49

Let \(x, y \in V(G)\).

  • \(\mathrm{edist}(x,y) = 0\) iff \(x = y\);

  • \(\mathrm{edist}(x,y) \neq \infty \) iff there exists a walk from \(x\) to \(y\).

Definition 50

The distance between \(x\) and \(y\) is \(\mathrm{dist}(x,y) = \mathrm{edist}(x,y).\mathrm{toNat}\).

Lemma 51

Let \(x, y \in V(G)\). \(\mathrm{dist}(x,y) \neq 0\) iff \(x \neq y\) and there exists a walk from \(x\) to \(y\).

Lemma 52

Let \(x, y \in V(G)\), we assume \(\mathrm{dist}(x,y) \neq 0\). Then there exists a walk of length \(\mathrm{dist}(x,y)\) from \(x\) to \(y\).

Definition 53

Let \(x \in V(G)\) and \(r \in \mathbb {N}\). We define the ball \(B_r(x)\) of radius \(r\) centered at \(x\) as the set of vertices \(y \in V(G)\) such that \(\mathrm{edist}(x,y)\leq r\).

Definition 54

Let \(x \in V(G)\) and \(r \in \mathbb {N}\). We define the circle \(C_r(x)\) of radius \(r\) centered at \(x\) as the set of vertices \(y \in V(G)\) such that \(\mathrm{edist}(x,y) = r\).

For any \(x \in V(G)\), \(B_0(x) = C_0(x) = \{ x\} \).

Lemma 56

For any \(x \in V(G)\), \(r \in \mathbb {N}\), \(C_r(x) \subseteq B_r(x)\) and if \(r \geq 1\) then \(C_r(x) = B_r(x) - B_{r-1}(x)\).

Lemma 57

Let \(x \in V(G)\) and \(r \in \mathbb {N}\). For any \(y \in C_{r+1}(x)\), there exists \(z \in C_r(x)\) such that \(y\) and \(z\) are adjacent.

Proof

Take \(y \in C_{r+1}(x)\). Then there exists a walk \((v_i)_{0 \leq i \leq r+1}\), \((e_i)_{1 \leq i \leq r+1}\) from \(x\) to \(y\). We take \(z=v_{r}\) and check it is adjacent to \(y\) and lies in \(C_r(x)\).

Assume \(G\) is locally bounded and let \(D = \sup _{x \in V(G)} \deg (x)\). Then, for any \(x \in V(G)\), any \(r \in \mathbb {N}\), \(\# B_r(x) \leq D^r\).

Proof

todo

1.1.8 Diameter

Definition 59

The extended diameter of a graph \(G\) is \(\mathrm{ediam}(G) := \sup _{x,y \in V(G)} \mathrm{edist}(x,y)\).

Definition 60

The diameter of \(G\) is \(\mathrm{diam}(G) = \mathrm{ediam}(G).\mathrm{toNat}\).

Assume \(V(G)\) is finite and non-empty. Then \(\mathrm{diam}(G) \neq 0\) if and only if \(G\) is connected.

Proof

To be adapted from SimpleGraph.

Assume \(G\) is a graph of finite diameter. For any \(x \in V(G)\), \(V(G)\) is included in the ball of radius \(\mathrm{diam}(G)\) centered at \(x\).

Proof

Let \(x, y \in V(G)\). By definition of the diameter, \(\mathrm{dist}(x,y) \leq \mathrm{diam}(G)\) and hence \(y\) lies in the ball of radius \(\mathrm{diam}(G)\) centered at \(x\).

Proposition 63

Let \(G\) be a regular graph of degree \(\delta \). Assume \(V(G)\) is finite and not empty. Then

\begin{equation} \mathrm{diam}(G) \geq \log _{\delta } \# V(G). \end{equation}
8

Proof

Let \(x \in V(G)\). By Lemma 62, if \(r := \mathrm{diam}(G)\), then \(V(G) \subseteq B_r(x)\). In particular \(\# V(G) \leq \# B_r(X) \leq \delta ^r\) by Proposition 58.

1.2 Space of functions on a graph

Let \(G\) be a graph.

Throughout this blueprint, we will be interested in the space \(\mathbb {C}^{V(G)}\) of functions \(V(G) \rightarrow \mathbb {C}\). This space is a \(\mathbb {C}\) vectorial space. Note that, in this context, \(0\) is the constant function equal to \(0\).

For the purpose of analysis, we will be interested in the Hilbert space \(\ell ^2(G) := \ell ^2(V(G))\), which is defined as the set of functions \(f : V(G) \rightarrow \mathbb {C}\) which are square-summable, equipped with the inner product

\[ \langle f, g \rangle = \sum _{x \in V(G)} f(x) \overline{g(x)}. \]

Note that, whenever \(V(G)\) is finite, \(\ell ^2(G) = \mathbb {C}^{V(G)}\) as any finite sequence is square-summable. The space \(\mathcal{B}(\ell ^2(G))\) of bounded linear operators on \(\ell ^2(G)\) is a unital \(\mathbb {C}^*\)-algebra.

Let us introduce several useful families of functions.

1.2.1 Constant fonctions

Definition 64
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A function \(V(G) \rightarrow \mathbb {C}\) is constant if, for all \(x, y \in V(G)\), \(f(x)=f(y)\).

Definition 65
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The constant function equal to \(1\) is defined by

\begin{equation} \mathbf{1}: \begin{cases} V(G) & \rightarrow \mathbb {C}\\ x & \mapsto 1. \end{cases} \end{equation}
9

Lemma 66

Assume \(V(G)\) is not empty. Then \(\mathbf{1}\neq 0\).

Proof

If \(V(G)\) is not empty, then there exists \(x \in V(G)\). Then \(\mathbf{1}(x) = 1 \neq 0\), and hence \(\mathbf{1}\neq 0\).

Theorem 67

Assume \(V(G)\) is not empty. Then the space of constant functions is a vectorial subspace of \(\mathbb {C}^{V(G)}\), of dimension \(1\), generated by \(\mathbf{1}\).

Proof

We directly check that:

  • the constant function equal to \(0\) is constant;

  • for any constant functions \(f_1\), \(f_2\) and any \(c \in \mathbb {C}\), the function \(f_1+cf_2\) is also constant;

both by direct application of the definition. We also check that the function \(\mathbf{1}\) is constant.

Let \(f : V(G) \rightarrow \mathbb {C}\) be a constant function. Since \(V(G)\) is not empty there exists \(x \in V(G)\). Then for all \(y \in V(G), f(y) = f(x) \mathbf{1}(y)\), so \(f = f(x) \mathbf{1}\), which allows to conclude.

Lemma 68

The space of constant functions is included in \(\ell ^2(G)\) if and only if \(V(G)\) is finite.

Lemma 69

Assume \(V(G)\) is non-empty and finite. Then, the orthogonal of the space of constant functions is

\begin{equation*} \mathbf{1}^\perp = \Big\{ f : V(G) \rightarrow \mathbb {C}: \sum _{x \in V(G)}f(x)=0 \Big\} . \end{equation*}
Proof

By definition of the orthogonal, \(\mathbf{1}^\perp \) is the space of functions \(f \in \ell ^2(G)\) such that \(\langle f, \mathbf{1}\rangle = 0\). Since \(V(G)\) is finite all functions \(V(G) \rightarrow \mathbb {C}\) are in \(\ell ^(G)\). By definition of the scalar product and the constant function, \(\langle f, \mathbf{1}\rangle = \sum _{x \in V(G)}f(x)\).

1.2.2 Indicator functions

Definition 70
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Let \(U\) be a subset of \(V(G)\). We define the indicator function \(\mathbf{1}_U : V(G) \rightarrow \mathbb {C}\) by

\begin{equation} \mathbf{1}_U (x) = \begin{cases} 1 & \text{if } x \in U \\ 0 & \text{otherwise}. \end{cases} \end{equation}
12

Lemma 71

The indicator function of a set \(U\) is in \(\ell ^2(G)\) iff \(U\) is finite.

Lemma 72

Let \((\pi _U)_{U \in I}\) be a partition of \(V(G)\). Then \(\mathbf{1}= \sum _{U \in I} \mathbf{1}_U\).

1.2.3 Locally constant functions

Definition 73
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A function \(f : V(G) \rightarrow \mathbb {C}\) is said to be locally constant if, for all \(x,y \in V(G)\), if \(x\) is adjacent to \(y\), then \(f(x)=f(y)\).

We have the following dart versions of the definition.

Lemma 74

Let \(f : V(G) \rightarrow \mathbb {C}\). The following are equivalent:

  1. \(f\) is locally constant;

  2. for every \(x \in V(G)\), for every \(d \in \mathrm{dartSet}(x)\), \(f(x)=f(\mathrm{end}(d))\).

  3. for every \(d \in D(G)\), \(f(\mathrm{start}(d))=f(\mathrm{end}(d))\).

Let \(f : V(G) \rightarrow \mathbb {C}\) be a locally constant function. For any \(x, y \in V(G)\), if \(x\) and \(y\) are reachable, then \(f(x)=f(y)\).

Proof

By induction.

A locally constant function on a preconnected graph is constant.

Proof

Let \(G\) be a preconnected graph and \(f : V(G) \rightarrow \mathbb {C}\) be a locally constant function. For any \(x\), \(y\) in \(V(G)\), by Definition 29, there exists a walk from \(x\) to \(y\). Lemma 75 this implies \(f(x)=f(y)\). Hence by Definition 64, \(f\) is constant.

Lemma 77

The restriction of a locally constant function to a subgraph is locally constant on that subgraph.

Proof

Let \(f\) be a locally constant function on \(G\), and \(H\) be a subgraph of \(G\). Let \(x, y\) be adjacent vertices in \(H\). By Lemma 14, \(x\) and \(y\) are also adjacent in \(G\). Then \(f(x)=f(y)\).

A function \(f\) is locally constant if and only if its restriction to each connected component is a constant function.

Proof

Assume \(f\) is locally constant. Let \(C\) be a connected component of \(G\). By Lemma 77, the restriction of \(f\) to \(C\) is locally constant. By Theorem 35, \(C\) is preconnected. So by Lemma 76, the restriction of \(f\) to \(C\) is constant.

Assume the restriction of \(f\) to each connected component is constant. Let \(x\), \(y\) be adjacent vertices. Then by Lemma 34, \(\mathrm{cc}(x) = \mathrm{cc}(y)\) which implies that \(y \in \mathrm{cc}(x)\). This allows to conclude since \(f\) is constant on \(\mathrm{cc}(x)\) by hypothesis and Theorem 35.

Lemma 79

The indicator function of a connected component is locally constant.

Lemma 80
#

The space of locally constant functions is a vectorial subspace of the space of functions \(V(G) \rightarrow \mathbb {C}\), and the \(\{ \mathbf{1}_C, C \in \mathrm{ConnComp}(G)\} \) are a basis of this space.

Proof

We directly check that:

  • the constant function equal to \(0\) is locally constant;

  • for any locally constant functions \(f_1\), \(f_2\) and any \(c \in \mathbb {C}\), the function \(f_1+cf_2\) is also locally constant;

both by direct application of the definition.

Now, for any connected component \(C\), \(\mathbf{1}_C\) is locally constant by Lemma 79.

We prove these functions form a basis using the fact that, by Proposition 37 and Lemma 72,

\begin{equation*} \mathbf{1}= \sum _{C \in \mathrm{ConnComp}(G)} \mathbf{1}_C \end{equation*}

and hence if \(f\) is locally constant,

\begin{equation*} \forall x \in V(G), f(x) = \sum _{C \in \mathrm{ConnComp}(G)} f(x) \mathbf{1}_C(x). \end{equation*}

The function \(f\) is constant on each connected component so this is a decomposition in the desired basis. The decomposition is unique as for all \(x \in V(G)\), the coefficient associated to \(\mathrm{cc}(x)\) must be \(f(x)\) since \(\mathbf{1}_C(x)=0\) for all other connected component.

Corollary 81
#

The dimension of the space of locally constant functions is the number of connected components of \(G\).

1.3 Adjacency operator

Let \(G\) be a graph.

1.3.1 Definition of the adjacency operator and action on constants

Assume \(G\) is locally finite.

Definition 82

We define the adjacency operator \(a\) as the linear operator acting on the space of functions \(f : V(G) \rightarrow \mathbb {C}\) by

\[ a f (x) := \sum _{d \in \mathrm{dartSet}(d)} f(\mathrm{end}(x)). \]

Let \(f\) be a locally constant function. Then, for all \(x \in V(G)\), \(af(x) = \deg (x) f(x)\).

Proof

Let \(x \in V(G)\). By definition,

\begin{equation*} af(x) = \sum _{d \in \mathrm{dartSet}(x)} f(\mathrm{end}(d)). \end{equation*}

By Lemma 5, for all \(d \in \mathrm{dartSet}(x)\), \(\mathrm{end}(d)\) is adjacent to \(x\) and in particular \(f(\mathrm{end}(d))=f(x)\). This leads to the conclusion by Definition 41.

Lemma 84

For any \(x \in V(G)\), \(a \mathbf{1}(x) = \deg (x)\).

1.3.2 Norm of the adjacency operator

Let \(G\) be a locally finite graph. The aim of this subsection is to prove the following.

Theorem 85
#

Assume \(G\) is locally bounded. Then the adjacency operator is a bounded operator on \(\ell ^2(G)\) and its norm satisfies \(\| a\| \leq \sup _{x \in V(G)} \deg (x)\).

Here is a key lemma.

For any function \(f : V(G) \rightarrow \mathbb {C}\), any vertex \(x \in V(G)\),

\begin{equation} |af(x)|^2 \leq \deg (x) \sum _{d \in \mathrm{dartSet}(x)} |f(\mathrm{end}(d))|^2. \end{equation}
15

Furthermore, this is an equality if and only if for any \(y, z\) adjacent to \(x\), \(f(y) = f(z)\).

Proof

Let \(f : V(G) \rightarrow \mathbb {C}\) and \(x \in V(G)\). By Definition 82,

\begin{equation} |af(x)|^2 = \Big| \sum _{d \in \mathrm{dartSet}(x)} f(\mathrm{end}(d))\Big|^2. \end{equation}
16

By the Cauchy-Schwarz inequality applied to the complex vectors \(v_1 := (1)_{d \in \mathrm{dartSet}(x)}\) and \(v_2 := (f(\mathrm{end}(d)))_{d \in \mathrm{dartSet}(x)}\) we have

\begin{align*} |af(x)|^2 & = \left| v_1 \cdot v_2 \right|^2 \leq \| v_1\| _2^2 \| v_2\| _2^2 \\ & \leq \left( \sum _{d \in \mathrm{dartSet}(x)} 1 \right) \left( \sum _{d \in \mathrm{dartSet}(x)} |f(\mathrm{end}(d))|^2 \right). \end{align*}

This leads to the inequality by Definition 41. The equality case comes from the equality case in Cauchy-Schwarz, which occurs if and only if the vectors \(v_1\) and \(v_2\) are colinear, which is equivalent to the announced condition as \(v_1\) is a non-zero constant vector, using Lemma 5.

Then Proposition 85 is a trivial consequence of the following lemma, together with the definition of bounded operator and operator norm.

Lemma 87

Assume \(G\) is locally bounded. Then, for any \(f \in \ell ^2(G)\), \(a f \in \ell ^2(G)\) and

\begin{equation} \| a f\| \leq \sup _{x \in V(G)} \deg (x) \times \| f\| . \end{equation}
17

Furthermore, the equality occurs if and only if \(G\) is regular of degree \(\delta \) for some non-negative integer \(\delta \) and, for any \(x \in V(G)\), for any \(y\) and \(z\) adjacent to \(x\), \(f(y)=f(z)\).

Proof

Let us write \(D := \sup _{x \in V(G)} \deg (x)\). By definition,

\begin{equation*} \| af\| ^2 = \sum _{x \in V(G)} |af(x)|^2 \end{equation*}

and \(af \in \ell ^2(G)\) iff this sum is finite. We sum Lemma 86 for all vertices of \(G\) and obtain that

\begin{equation*} \_ \leq \sum _{x \in V(G)} \deg (x) \sum _{d \in \mathrm{dartSet}(x)} |f(\mathrm{end}(d))|^2 \leq D \sum _{d \in D(G)} |f(\mathrm{end}(d))|^2. \end{equation*}

We apply the involution \(\mathrm{symm}\) to the sum, changing indices, to obtain

\begin{equation*} \_ = D \sum _{d \in D(G)} |f(\mathrm{end}(\mathrm{symm}(d)))|^2 = D \sum _{d \in D(G)} |f(\mathrm{start}(d))|^2. \end{equation*}

Now by Definitions 3 and 41

\begin{equation*} \_ = D \sum _{x \in V(G)} \sum _{x \in \mathrm{dartSet}(x)} |f(\mathrm{start}(d))|^2 = D \sum _{x \in V(G)} \deg (x) |f(x)|^2 \leq D^2 \sum _{x \in V(G)} |f(x)|^2 = D^2 \| f\| ^2. \end{equation*}

This means that \(af \in \ell ^2(G)\) and \(\| af\| \leq D \| f\| \), which was our claim. If the equality occurs, since all terms are non-negative, it means that \(\deg (x)\) is constant (i.e. the graph is regular), and that there is equality in each term of Lemma 86, which is the conclusion.

1.3.3 Adjacency operator is self-adjoint

Let \(G\) be a locally bounded graph.

Proposition 88
#

The adjacency operator is selfadjoint.

Here is the key lemma.

Lemma 89
#

For any \(f\), \(g\) in \(\ell ^2(G)\),

\begin{equation} \langle af, g \rangle = \sum _{d \in D(G)} f(\mathrm{end}(d)) \overline{g(\mathrm{start}(d))}. \end{equation}
18

Proof

Let \(f\), \(g\) be elements of \(\ell ^2(G)\). By Proposition 87, \(a f \in \ell ^2(G)\). By definition of the scalar product,

\begin{equation} \langle af, g \rangle = \sum _{x \in V(G)} af(x) \overline{g(x)}. \end{equation}
19

By Definition 82,

\begin{equation} \langle af, g \rangle = \sum _{x \in V(G)} \sum _{d \in \mathrm{dartSet}(x)} f(\mathrm{end}(d)) \overline{g(x)} \end{equation}
20

which we can rewrite as the claim.

We are now ready to prove Proposition 88.

Proof

Let \(f\), \(g\) be elements of \(\ell ^2(G)\). We have by Lemma 89

\begin{equation*} \langle af, g \rangle = \sum _{d \in D(G)} f(\mathrm{end}(d)) \overline{g(\mathrm{start}(d))} = \overline{\sum _{d \in D(G)} g(\mathrm{end}(d)) \overline{f(\mathrm{start}(d))}} \end{equation*}

by complex conjugation and applying the involution \(\mathrm{symm}\) to exchange the start and end of each dart. Hence by Lemma 89

\begin{equation*} \langle af, g \rangle = \overline{\langle ag, f \rangle } = \langle f, ag \rangle . \end{equation*}

This means that \(a^*=a\) by definition of the adjoint, which means \(a\) is self-adjoint by definition.

Corollary 90

The spectrum of the adjacency operator is real.

Proof

This follows from Proposition 88 with selfAdjoint.mem_spectrum_eq_re.

Lemma 91

The spectrum of \(a\) is included in the segment \([-D,D]\) where \(D = \sup _{x \in V(G)} \deg (x)\).

Proof

By Corollary 90, the spectrum of \(a\) is real. By spectrum.subset_closedBall_norm, it is included in the closed ball of center \(0\) and radius \(\| a\| \). We have \(\| a\| \leq D\) by Theorem 85.

If \(V(G)\) is finite, then the spectrum of the adjacency operator is a family of \(\# V(G)\) eigenvalues.

Proof

If \(V(G)\) is finite, then \(\ell ^2(G)\) has finite dimension. So this is just the spectral theorem for self-adjoint operators in finite dimension.

1.3.4 From adjacency operator to graph

We now introduce a construction allowing to go from adjacency operator to graph, which will be useful to define random graphs.

We first make a few observations as to the properties of adjacency operators on locally finite graphs.

Let \(x, y \in V(G)\). Then

\begin{equation*} \langle a \mathbf{1}_{\{ x\} }, \mathbf{1}_{\{ y\} } \rangle = \# \mathrm{dartSet}(x,y). \end{equation*}
Proof

The functions \(\mathbf{1}_{\{ x\} }\) and \(\mathbf{1}_{\{ y\} }\) are trivially in \(\ell ^2(G)\). By Lemma 89,

\begin{equation*} \langle a \mathbf{1}_{\{ x\} }, \mathbf{1}_{\{ y\} } \rangle = \sum _{d \in D(G)} \mathbf{1}_{\{ x\} }(\mathrm{start}(d)) \overline{\mathbf{1}_{\{ y\} }(\mathrm{end}(d))} \end{equation*}

which is exactly the claim by Definition 4 and Definition 70.

Lemma 94

For any \(x, y \in V(G)\), \(\langle a \mathbf{1}_{\{ x\} }, \mathbf{1}_{\{ y\} } \rangle \) is a non-negative integer, which is even as soon as \(x=y\).

Proof

By Lemma 93, this quantity is a cardinal of a set, and hence a non-negative integer. The parity when \(x=y\) comes from Lemma 42.

Definition 95
#

Let \(V\) be a set. An operator \(a : V^{\mathbb {C}} \rightarrow V^{\mathbb {C}}\) is adjacency-like if it satisfies the following properties:

  • for all \(x \in V\), \(a \mathbf{1}_{\{ x\} } \in \ell ^2(G)\);

  • for all \(x, y \in V\), \(a_{x,y} := \langle a \mathbf{1}_{\{ x\} }, \mathbf{1}_{\{ y\} } \rangle \) is a non-negative integer;

  • for all \(x, y \in V\), \(a_{x,y}=a_{y,x}\);

  • for all \(x \in V\), \(a_{x,x}\) is even.

We then associate to any adjacent-like operator a dart-like structure.

Definition 96

Let \(V\) be a set and \(a\) be an adjacency-like operator on \(V\). We define a dart-like structure on \(V\) by taking:

  • \(D\) is the set of \((x,y,i)\) where \(x \in V\), \(y \in V\), and \(i\) is an integer such that

    • if \(x \neq y\) then \(1 \leq i \leq a_{x,y}\);

    • if \(x=y\) then \(1 \leq |i| \leq a_{x,x}\).

  • for any \((x,y,i) \in D\), we let \(\mathrm{start}(x,y,i)=x\), \(\mathrm{end}(x,y,i)=y\) and

    \begin{equation*} \mathrm{symm}(x,y,i) := \begin{cases} \mathrm{symm}(y,x,i) & \text{if } x \neq y \\ \mathrm{symm}(x,y,-i) & \text{if } x = y. \end{cases}\end{equation*}
Definition 97

Let \(a\) be a adjacency-like operator on \(V\). We define the graph associated to \(a\) by taking the graph associated to the dartlike structure associated to \(a\).

Lemma 98

Let \(a\) be a adjacency-like operator on \(V\). Then the graph \(G\) associated to \(a\) is a graph, of vertex set \(V\), and adjacency operator \(a\).

Lemma 99

Let \(V\) be a set. The space of adjacency-like operators on \(V\) is stable by addition, i.e. if \(a\), \(a'\) are adjacency-like, so is \(a+a'\).

1.4 Permutation graphs

Let \(V\) be a finite set and \(\mathfrak {S}_{V} = \{ \pi : V \rightarrow V \text{ bijective}\} \) the set of permutations of \(V\).

We define a graph associated to a family of permutations.

Definition 100

Let \(k \geq 1\) be an integer. To a family \(\vec{\pi } = (\pi _1, \ldots , \pi _k) \in \mathfrak {S}_{V}^k\) we associate a dart-like structure on \(V\) by taking:

  • \(D = V \times \{ 1, \ldots , k\} \times \{ -1,+1\} \)

  • for \((x,i,\epsilon ) \in D\), we let \(\mathrm{start}(x,i,\epsilon ) = x\), \(\mathrm{end}(x,i,\epsilon ) = \pi _i^\epsilon (x)\) and \(\mathrm{symm}(x,i,\epsilon ) = (\pi _i^\epsilon (x),i,-\epsilon )\).

Proof

We check that

\begin{equation*} \mathrm{start}(\mathrm{symm}(x,i,\epsilon )) = \mathrm{start}(\pi _i^\epsilon (x),i,-\epsilon ) = \pi _i^\epsilon (x) = \mathrm{end}(x,i,\epsilon ) \end{equation*}

and

\begin{equation*} \mathrm{end}(\mathrm{symm}(x,i,\epsilon )) = \mathrm{end}(\pi _i^\epsilon (x),i,-\epsilon ) = \pi _i^{-\epsilon }(\pi _i^\epsilon (x)) = x = \mathrm{start}(x,i,\epsilon ) \end{equation*}

and

\begin{equation*} \mathrm{symm}(\mathrm{symm}(x,i,\epsilon )) = \mathrm{symm}((\pi _i^\epsilon (x),i,-\epsilon )) = (\pi _i^{-\epsilon }(\pi _i^\epsilon (x)),i,–\epsilon ) = (x,i,\epsilon ). \end{equation*}
Definition 101

Let \(k \geq 1\) be an integer. To a family \(\vec{\pi } \in \mathfrak {S}_{V}^k\) we associate a graph \(G_{\vec{\pi }}\) on \(V\) by taking the graph associated to the dart-like structure associated to \(\vec{\pi }\).

Lemma 102

Let \(k \geq 1\) be an integer and take \(\vec{\pi } = (\pi _1, \ldots , \pi _k) \in \mathfrak {S}_{V}^k\). Then, for any \(x, y \in V\),

\begin{equation*} \mathrm{dartSet}(x,y) = \{ (x,i,\epsilon ): y = \pi _i^\epsilon (x)\} . \end{equation*}
Lemma 103
#

Let \(k \geq 1\) be an integer and take \(\vec{\pi } = (\pi _1, \ldots , \pi _k) \in \mathfrak {S}_{V}^k\). Then, for any \(x \in V\),

\begin{equation*} \mathrm{dartSet}(x) = \{ (x,i,\epsilon ): 1 \leq i \leq k, \epsilon = \pm 1\} . \end{equation*}
Lemma 104

The graph \(G_{\vec{\pi }}\) is regular of degree \(2k\).

[I need to decide the conventions about the side of the actions below]

Definition 105
#

For \(\pi \in \mathfrak {S}_{V}\), we define an operator \(u_\pi : V^{\mathbb {C}} \rightarrow V^{\mathbb {C}}\) by letting, for \(f : V \rightarrow \mathbb {C}\),

\begin{equation*} \forall x \in V, u_\pi f(x) = f(\pi ^{-1}(x)) \end{equation*}
Lemma 106

For \(\pi \in \mathfrak {S}_{V}\), the operator \(u_\pi \) is a unitary operator on \(\ell ^2(V)\) and \(u_\pi ^*=u_\pi ^{-1}=u_{\pi ^{-1}}\).

Let \(k \geq 1\) be an integer. For any family \(\vec{\pi } = (\pi _1, \ldots , \pi _k) \in \mathfrak {S}_{V}^k\), the adjacency operator \(a_{\vec{\pi }}\) of \(G_{\vec{\pi }}\) can be written as

\begin{equation*} a_{\vec{\pi }} = \sum _{i=1}^k (u_{\pi _i} + u_{\pi _i}^*). \end{equation*}
Proof

We have by definition of the adjacency operator for a function \(f : V \rightarrow \mathbb {C}\) and \(x \in V\),

\begin{equation*} af(x) = \sum _{d \in \mathrm{dartSet}(x)} f(\mathrm{end}(d)) = \sum _{i=1}^k \sum _{\epsilon = \pm 1} f(\pi _i^\epsilon (x)) = \sum _{i=1}^k (f(\pi _i^{-1}(x)) + f(\pi _i(x))) \end{equation*}

which leads to the conclusion as we recognise \(u_{\pi _i}\) and \(u_{\pi _i^{-1}} = u_{\pi _i}^*\).

In what follows, we write \(\mathfrak {S}_{n}\) for the set of permutations of \([n]\).

Definition 108

Let \(k, n \geq 1\) be integers. A random permutation graph of degree \(2k\) with \(n\) vertices is the graph associated to a family \(\vec{\pi } = (\pi _1, \ldots , \pi _k) \in \mathfrak {S}_{n}\) of \(k\) i.i.d. uniform elements of \(\mathfrak {S}_{n}\). We denote by \(\mathrm{Prob}_{n,\delta }\) the corresponding probability measure on the space of \(\delta \) regular graphs with vertex set \([n]\).