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.
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 \(d \in D(G)\), \(\mathrm{symm}(d) \neq d\).
For any \(d \in D(G)\), \(\mathrm{symm}(\mathrm{symm}(d))=d\).
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.
Let \(x \in V(G)\). The dartset of \(x\) is the set
Let \(x\), \(y \in V(G)\). We define the dartset of \((x,y)\) as
Let \(x, y \in V(G)\). The following are equivalent:
\(x\) and \(y\) are adjacent;
there exists \(d \in \mathrm{dartSet}(x)\) such that \(\mathrm{end}(d)=y\);
\(\mathrm{dartSet}(x,y)\) is not empty.
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.
For any \(e \in E(G)\), there exists \(d \in D(G)\) such that \(\mathrm{edge}^{-1}(\{ e\} ) = \{ d, \mathrm{symm}(d)\} \).
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.
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 \(x=y\).
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.
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.
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:
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).
This is a classic fact for involutions with no fixed points.
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 \(\mathrm{end}(d)\).
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 \(\mathrm{end}\circ \mathrm{symm}= \mathrm{start}\).
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.
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 \(G\).
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 \(G\).
We define the union, resp. intersection of two subgraphs by taking the union of their respective vertex sets and edge sets.
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 \(e \in E(H)\).
Let \(U\) be a subset of \(V(G)\). Then we define a subgraph \(H\) induced by \(U\) by letting:
\(V(H) := U\);
\(E(H) := \{ e \in E(G) : \text{ both endpoints of }e \text{ lie in } U\} \).
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.
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 \(v_i\).
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?
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 \(1 \leq i {\lt} m\), \(\mathrm{end}(d_i)=\mathrm{start}(d_{i+1})\).
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 \(1 \leq i \leq m\).
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.
The reachability relation is an equivalence relation on \(V(G)\).
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 \(i \in \{ 0, \ldots , m'\} \)
and for \(i \in \{ 1, \ldots , m'\} \)
Then this is a walk from \(x\) to \(z\), hence \(x \sim z\).
Let \(x\), \(y\) in \(V(G)\). If \(x\) and \(y\) are adjacent then \(x\) and \(y\) are reachable.
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 \(y\).
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 \(i \neq j\), \(v_i \neq v_j\).
A dartwalk \((d_i)_{1 \leq i \leq k}\) is a dartpath if all vertices in its support are distinct.
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.
A graph is preconnected if any pair of vertices is reachable.
The graph \(G\) is connected if it is preconnected and \(V(G)\) is not empty.
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.
A connected component of \(G\) is an equivalence class of \(V(G)\) for the relation reachable.
For any vertex \(x\), we define \(\mathrm{cc}(x)\) to be the unique connected component of \(G\) containing \(x\).
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.
We denote as \(\mathrm{ConnComp}(G)\) the set of connected components of \(G\).
\(\mathrm{ConnComp}(G)\) is a partition of \(V(G)\).
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
Let \(x\) be a vertex of \(G\). We say \(G\) is locally finite at \(x\) if \(\mathrm{dartSet}(x)\) is finite.
\(G\) is said to be locally finite if, for all \(x \in V(G)\), \(G\) is locally finite at \(x\).
Let \(x\) be a vertex of \(G\). We assume \(G\) is locally finite at \(x\). The degree of \(x\) is defined as \(\mathrm{deg}(x) = \# \mathrm{dartSet}(x)\).
If \(G\) is locally finite at \(x \in V(G)\), then \(\# \mathrm{dartSet}(x,x)\) is an even number.
Let \(x \in V(G)\). \(G\) is locally finite at \(x\) iff \(\mathrm{incidenceSet}(x)\) is finite, and in this case,
Using Lemma 7,
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 \(\mathrm{loopSet}(x) \subseteq \mathrm{incidenceSet}(x)\).
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 \(x \in V(G)\), \(\deg (x) = \delta \).
The graph \(G\) is regular of degree \(0\) iff \(E(G)=\emptyset \).
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
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
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 \(\mathbb {N}\cup \{ \infty \} \)).
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 \(y\).
The distance between \(x\) and \(y\) is \(\mathrm{dist}(x,y) = \mathrm{edist}(x,y).\mathrm{toNat}\).
Let \(x, y \in V(G)\). \(\mathrm{dist}(x,y) \neq 0\) iff \(x \neq y\) and there exists a walk from \(x\) to \(y\).
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 \(y\).
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 \(\mathrm{edist}(x,y)\leq r\).
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 \(x \in V(G)\), \(B_0(x) = C_0(x) = \{ x\} \).
For any \(x \in V(G)\), \(r \in \mathbb {N}\), \(C_r(x) \subseteq B_r(x)\) and if \(r \geq 1\) then \(C_r(x) = B_r(x) - B_{r-1}(x)\).
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.
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 \(r \in \mathbb {N}\), \(\# B_r(x) \leq D^r\).
todo
1.1.8 Diameter
The extended diameter of a graph \(G\) is \(\mathrm{ediam}(G) := \sup _{x,y \in V(G)} \mathrm{edist}(x,y)\).
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.
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 \(x\).
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 \(x\).
Let \(G\) be a regular graph of degree \(\delta \). Assume \(V(G)\) is finite and not empty. Then
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
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
A function \(V(G) \rightarrow \mathbb {C}\) is constant if, for all \(x, y \in V(G)\), \(f(x)=f(y)\).
The constant function equal to \(1\) is defined by
Assume \(V(G)\) is not empty. Then \(\mathbf{1}\neq 0\).
If \(V(G)\) is not empty, then there exists \(x \in V(G)\). Then \(\mathbf{1}(x) = 1 \neq 0\), and hence \(\mathbf{1}\neq 0\).
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 \(\mathbf{1}\).
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.
The space of constant functions is included in \(\ell ^2(G)\) if and only if \(V(G)\) is finite.
Assume \(V(G)\) is non-empty and finite. Then, the orthogonal of the space of constant functions is
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
Let \(U\) be a subset of \(V(G)\). We define the indicator function \(\mathbf{1}_U : V(G) \rightarrow \mathbb {C}\) by
The indicator function of a set \(U\) is in \(\ell ^2(G)\) iff \(U\) is finite.
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
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.
Let \(f : V(G) \rightarrow \mathbb {C}\). The following are equivalent:
\(f\) is locally constant;
for every \(x \in V(G)\), for every \(d \in \mathrm{dartSet}(x)\), \(f(x)=f(\mathrm{end}(d))\).
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 \(f(x)=f(y)\).
By induction.
A locally constant function on a preconnected graph is constant.
The restriction of a locally constant function to a subgraph is locally constant on that subgraph.
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.
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.
The indicator function of a connected component is locally constant.
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.
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,
and hence if \(f\) is locally constant,
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.
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.
We define the adjacency operator \(a\) as the linear operator acting on the space of functions \(f : V(G) \rightarrow \mathbb {C}\) by
Let \(f\) be a locally constant function. Then, for all \(x \in V(G)\), \(af(x) = \deg (x) f(x)\).
Let \(x \in V(G)\). By definition,
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.
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.
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 \(x \in V(G)\),
Furthermore, this is an equality if and only if for any \(y, z\) adjacent to \(x\), \(f(y) = f(z)\).
Let \(f : V(G) \rightarrow \mathbb {C}\) and \(x \in V(G)\). By Definition 82,
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
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.
Assume \(G\) is locally bounded. Then, for any \(f \in \ell ^2(G)\), \(a f \in \ell ^2(G)\) and
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 \(x\), \(f(y)=f(z)\).
Let us write \(D := \sup _{x \in V(G)} \deg (x)\). By definition,
and \(af \in \ell ^2(G)\) iff this sum is finite. We sum Lemma 86 for all vertices of \(G\) and obtain that
We apply the involution \(\mathrm{symm}\) to the sum, changing indices, to obtain
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.
The adjacency operator is selfadjoint.
Here is the key lemma.
For any \(f\), \(g\) in \(\ell ^2(G)\),
Let \(f\), \(g\) be elements of \(\ell ^2(G)\). By Proposition 87, \(a f \in \ell ^2(G)\). By definition of the scalar product,
By Definition 82,
which we can rewrite as the claim.
We are now ready to prove Proposition 88.
Let \(f\), \(g\) be elements of \(\ell ^2(G)\). We have by Lemma 89
by complex conjugation and applying the involution \(\mathrm{symm}\) to exchange the start and end of each dart. Hence by Lemma 89
This means that \(a^*=a\) by definition of the adjoint, which means \(a\) is self-adjoint by definition.
The spectrum of the adjacency operator is real.
This follows from Proposition 88 with selfAdjoint.mem_spectrum_eq_re.
The spectrum of \(a\) is included in the segment \([-D,D]\) where \(D = \sup _{x \in V(G)} \deg (x)\).
If \(V(G)\) is finite, then the spectrum of the adjacency operator is a family of \(\# V(G)\) eigenvalues.
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
The functions \(\mathbf{1}_{\{ x\} }\) and \(\mathbf{1}_{\{ y\} }\) are trivially in \(\ell ^2(G)\). By Lemma 89,
which is exactly the claim by Definition 4 and Definition 70.
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 \(x=y\).
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.
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*}
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 \(a\).
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 \(a\).
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.
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 \(\mathrm{symm}(x,i,\epsilon ) = (\pi _i^\epsilon (x),i,-\epsilon )\).
We check that
and
and
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 \(\vec{\pi }\).
Let \(k \geq 1\) be an integer and take \(\vec{\pi } = (\pi _1, \ldots , \pi _k) \in \mathfrak {S}_{V}^k\). Then, for any \(x, y \in V\),
Let \(k \geq 1\) be an integer and take \(\vec{\pi } = (\pi _1, \ldots , \pi _k) \in \mathfrak {S}_{V}^k\). Then, for any \(x \in V\),
The graph \(G_{\vec{\pi }}\) is regular of degree \(2k\).
[I need to decide the conventions about the side of the actions below]
For \(\pi \in \mathfrak {S}_{V}\), we define an operator \(u_\pi : V^{\mathbb {C}} \rightarrow V^{\mathbb {C}}\) by letting, for \(f : V \rightarrow \mathbb {C}\),
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
We have by definition of the adjacency operator for a function \(f : V \rightarrow \mathbb {C}\) and \(x \in V\),
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 \([n]\).
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 \([n]\).