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Classical Potts random clusters and planar loop transfer

1 · Prerequisites

2 · Summary

3 · Logical flowchart

4 · Definitions, postulates, theorems and proofs

DefinitionDefinition: AI-adaptedProof: Not applicableprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Finite Potts and Fortuin–Kasteleyn weights

Definition

Let G=(V,E) be a finite simple graph, so V is finite and E is a set of two-element subsets of V. Fix an integer Q≥2, write [Q]={1,…,Q}, and assign to each edge e∈E a finite activity ve∈[0,∞). A spin configuration is a function σ:V→[Q]. Its unnormalized Potts weight and partition function are wP(σ):=∏e={ue,we}∈E(1+ve1{σ(ue)=σ(we)}),ZP(Q,v):=∑σ:V→[Q]wP(σ). For A⊆E, let GA=(V,A), and let kG(A) be its number of connected components, counting isolated vertices; equivalently, these are the equivalence classes generated by the endpoint pairs of edges in A. Put vA:=∏e∈Ave, with empty product 1. The unnormalized Fortuin–Kasteleyn (FK) weight and partition function are wFK(A):=QkG(A)vA,ZFK(Q,v):=∑A⊆EwFK(A). Both normalizers are finite and positive: the spin sum is finite with every term at least 1, while the empty-edge FK term is Q∣V∣>0. These are finite mathematical weights; no physical units or empirical status are assigned.

TheoremStatement: AI-adaptedProof: AI-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Finite Edwards–Sokal coupling and correlation identity

Statement

Let G=(V,E) be a finite simple graph, let Q≥2 be an integer, and let each edge activity ve be finite and nonnegative. Use the Potts weight wP(σ)=∏e={ue,we}(1+ve1{σ(ue)=σ(we)}) and FK weight wFK(A)=QkG(A)∏e∈Ave from the finite definitions. Define W(σ,A):=∏e∉A1∏e∈Ave1{σ(ue)=σ(we)},(σ,A)∈[Q]V×2E. The normalized joint law ν(σ,A):=W(σ,A)/∑τ,BW(τ,B) has Potts marginal wP/ZP and FK marginal wFK/ZFK, with ZP=ZFK=∑τ,BW(τ,B). Conditional on an edge set A of positive probability, the colors of its connected components are independent and uniform on [Q]. Thus for every x,y∈V, P(σ(x)=σ(y))=1Q+(1−1Q)PFK(x↔y), where x↔y means that x and y lie in the same component of GA.

Facts & Assumptions

Given: A finite simple graph G=(V,E), integer Q≥2, and finite activities ve≥0.

[F1]

Finite Potts and Fortuin–Kasteleyn weights: the spin weight is ∏e(1+ve1{σ(ue)=σ(we)}), the FK weight of A is QkG(A)∏e∈Ave, and both finite normalizers are positive.

Proof

technique · direct
1.1givenF1algebra

For fixed σ, summing W(σ,A) over all A⊆E chooses, independently for each edge, either its absent factor 1 or its present factor ve1{σ(ue)=σ(we)}. Distributivity of the finite product therefore gives ∑AW(σ,A)=wP(σ). Summing this equality over the finite set [Q]V gives ∑σ,AW(σ,A)=ZP.

1.2F1algebra

Fix A⊆E. The product W(σ,A) is zero unless the spins agree across every edge in A. Equality propagates along each finite path, so the admissible configurations are exactly those constant on each connected component of GA. Assigning one of Q colors independently to each of its kG(A) components gives exactly QkG(A) such configurations, each with weight ∏e∈Ave. Hence ∑σW(σ,A)=wFK(A), including when this weight is zero. Summing over A gives ∑σ,AW(σ,A)=ZFK.

2.1F1step 1.1step 1.2algebra

The common total is positive by [F1], so dividing W by it defines a probability law. The finite marginal sums in steps 1.1 and 1.2 show that its spin and edge marginals are respectively wP/ZP and wFK/ZFK, and that ZP=ZFK.

2.2step 1.2algebra

For a fixed A with positive FK probability, every spin assignment constant on its components has the same joint weight ∏e∈Ave. By step 1.2 there are QkG(A) such assignments, so the conditional law is uniform on them; this is equivalent to assigning independent uniform colors to the components.

3.1step 2.1step 2.2algebra∎

If x,y are connected in GA, step 2.2 makes their colors equal with conditional probability 1. If they are in distinct components, their independent uniform colors agree with probability 1/Q. Averaging over the FK marginal from step 2.1 yields P(σ(x)=σ(y))=PFK(x↔y)+Q−1PFK(x↮y)=Q−1+(1−Q−1)PFK(x↔y).

DefinitionDefinition: AI-adaptedProof: Not applicableprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Finite FK boundary wiring

Definition

Let G=(V,E) be a finite simple graph, let Q≥2 be an integer, and give each edge a finite activity ve≥0. Choose a set B⊆V of boundary vertices and an equivalence relation ξ on B. The relation ξ specifies which boundary vertices are identified before any edges are occupied. For A⊆E, let ∼ξ,A be the least equivalence relation on V containing every pair in ξ and both endpoints of every edge in A. Write kξ(A):=∣V/∼ξ,A∣ and vA:=∏e∈Ave, with empty product 1. Define wFKξ(A):=Qkξ(A)vA,ZFKξ(Q,v):=∑A⊆EwFKξ(A). The free boundary choice is the discrete relation on B (each boundary vertex is a singleton); the wired classes are the equivalence classes of ξ, with a fully wired boundary given by one class when B≠∅. Thus kξ(A) counts components after the prescribed identifications and occupied edges, including isolated vertices and boundary classes. The sum is finite and positive because its empty-edge term is Qkξ(∅)>0. For discrete ξ, kξ(A)=kG(A) and ZFKξ=ZFK. This is a mathematical partition polynomial; no wired Potts spin marginal is asserted.

AlgorithmAlgorithm: AI-adaptedProof: Not applicableprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Finite FK connectivity-state transfer

Definition

Input a finite simple graph G=(V,E), integer Q≥2, finite activities ve∈[0,∞), a total edge order e1,…,em, and boundary data consisting of a set B⊆V and an equivalence relation ξ on B. The relation ξ identifies prescribed boundary vertices before any edge is processed; its singleton classes give free boundary and its non-singleton classes give the wired classes. A vertex incident to no edge is isolated. Put n0:=∣{x∈V∖B:x is isolated}∣. At time t, the active frontier consists of the initially active boundary vertices B together with vertices introduced at a processed edge, minus those already forgotten. A state is a set partition of the active frontier. Its blocks record equivalence generated by the initial boundary relation ξ and processed present edges. A coefficient vector assigns a nonnegative real weight to each such partition.

Initialize the frontier as B with partition ξ and coefficient Qn0; if B=∅, this is the empty partition. For t=1,…,m, perform the following operations in order.

  1. Introduce each endpoint of et that is not yet active as a singleton block.
  2. For each state π of coefficient c, add c to the same state (the edge is absent) and add cvet to Juvπ (the edge is present), where et={u,v} and Juvπ merges the blocks containing u and v and leaves all other blocks unchanged. If u,v already share a block, Juvπ=π. Thus Juv2=Juv and the edge operator is I+vetJuv.
  3. Forget vertices whose last incident edge is et. When removing a vertex empties its block, delete that block and multiply its coefficient by Q once; otherwise just remove the vertex from its block. If several vertices in a block are forgotten at this step, the factor Q is applied only when the last active member is removed.

After all edges are processed, forget any remaining active vertices by the same rule and return the coefficient of the empty partition. This includes boundary vertices with no incident edge; each surviving wired component is then counted once. Discrete ξ gives free boundary, while non-singleton classes implement the stated wired boundary condition. A planar-strip annotation consists of a plane embedding, an ordered frontier list at every update, a noncrossing initial relation ξ, and the condition that every reachable connectivity partition is noncrossing. For an ordered frontier, “noncrossing” means that no two different blocks contain alternating points a<b<c<d with a,c in one block and b,d in the other. When u,v are adjacent frontier points i,i+1, write Ji for Juv; these are the noncrossing connectivity states and adjacent-block joins used for planar transfer. The general procedure stores all set partitions when no such annotation is supplied. All arithmetic is exact; each input has finitely many edge updates and finitely many partitions at every frontier.

TheoremStatement: AI-adaptedProof: AI-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Correctness of the finite FK connectivity transfer

Statement

For every finite simple graph G=(V,E), integer Q≥2, finite activities ve≥0, edge order, boundary set B⊆V, and equivalence relation ξ on B, the finite FK connectivity-transfer procedure terminates after finitely many operations and returns exactly ZFKξ(Q,v)=∑A⊆EQkξ(A)∏e∈Ave, where kξ(A) counts equivalence classes generated by ξ and the endpoint pairs of occupied edges. In the free case, where ξ is discrete, ZFK(Q,v)=∑A⊆EQkG(A)∏e∈Ave by the finite Potts/FK definition, so the transfer returns the finite FK partition function exactly.

Facts & Assumptions

Given: A finite simple graph G=(V,E), integer Q≥2, finite activities ve≥0, an ordered edge list e1,…,em, boundary set B⊆V, and equivalence relation ξ on B.

[F1]

Finite FK connectivity-state transfer: initialize the frontier with boundary partition ξ and factor Q for each isolated vertex outside B; each edge branches into absent weight 1 and present weight ve with the endpoint blocks merged; a block receives one factor Q when its last active vertex is forgotten, including at final cleanup.

[F2]

Finite FK boundary wiring: kξ(A) is the number of equivalence classes generated by ξ and the endpoints of edges in A, and ZFKξ=∑A⊆EQkξ(A)vA.

[F3]

Finite Potts and Fortuin–Kasteleyn weights: when ξ is discrete, kξ(A)=kG(A) and the resulting partition function is ZFK, including isolated vertices.

Proof

technique · direct invariant
1.1givenF1construct

Let Et={e1,…,et} and let n0 count vertices outside B that are isolated in G. For each edge subset C⊆Et, form the graph on the initially present boundary vertices and the endpoints introduced by time t, impose ξ, and retain the present edges in C. Let πt(C) be the resulting equivalence partition restricted to the active frontier, and let ct(C) count its equivalence classes containing no active vertex. The coefficient vector after time t satisfies Wt(π)=Qn0∑C⊆Etπt(C)=πQct(C)∏e∈Cve. At t=0 the only subset is empty, every boundary vertex is active, their partition is ξ, and c0=0. The initialized coefficient is Qn0, so the invariant holds.

2.1F1step 1.1algebra

Assume the invariant after t−1 edges. Introducing a new endpoint as a singleton agrees with adding that previously unseen vertex to the partial graph; a boundary endpoint is already present in the initial relation ξ. For each partial subset C⊆Et−1, the two subsets at the next edge are exactly C and C∪{et}. The absent choice preserves the partition and weight; the present choice multiplies by vet and merges precisely the endpoint blocks. Thus I+vetJuv gives the two required contributions, even when u,v were already equivalent through ξ or previous edges.

3.1F1step 2.1algebra

When vertices at their last incident edge are forgotten, no future edge is incident to them. If their block still has an active vertex, that equivalence class can connect to future edges through that vertex and is not yet closed. If removing a vertex empties its block, every boundary vertex in its ξ-class was already initially active and is now absent as well; no future edge can meet any member of the class. The class is therefore final, and the algorithm multiplies by Q exactly once at this first emptying. This updates ct(C) and preserves the invariant. After the last edge, terminal cleanup applies the same criterion to the remaining blocks, which have no future incident edges.

4.1F2step 1.1step 3.1algebra

For each full present-edge subset C, let rm(C) be the number of blocks still active just before terminal cleanup. Every equivalence class generated by ξ and C is counted exactly once: isolated vertices outside B contribute n0, classes closed earlier contribute cm(C), and the rm(C) remaining blocks each contribute one factor Q in cleanup. Therefore n0+cm(C)+rm(C)=kξ(C), and summing the invariant from step 1.1 with these cleanup factors over C⊆E gives the returned scalar ∑CQkξ(C)vC=ZFKξ.

5.1F3step 4.1givenalgebra∎

Each of the finitely many m edges is processed once; every active frontier is finite and has finitely many set partitions, and terminal cleanup removes finitely many vertices. Hence the procedure terminates. For discrete ξ, [F3] identifies ZFKξ with ZFK.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)Open item page →

Classical FK medial-loop normalization

Statement

Let G=(V,E) be a finite connected simple plane graph, let F(G) be its face set, and choose its unbounded face as the free exterior face. All vertices are free: no boundary vertices are identified or fixed. Fix an integer Q≥2 and finite edge activities ve≥0. For A⊆E, write GA=(V,A) and let k(A) count its connected components, including isolated vertices. Let G∗ be the dual multigraph with one vertex for each face and one dual edge e∗ crossing each primal edge e; loops and parallel edges are retained. Set A∗:={e∗:e∈E∖A} and let k∗(A) count the connected components of the spanning dual subgraph (F(G),A∗), counting isolated faces; the unbounded exterior face is one dual vertex counted once.

The medial graph has one four-valent vertex at each primal-dual edge crossing, joined along the corners of the overlay. At each crossing label the four medial half-edges h1,h2,h3,h4 in cyclic order, with h1,h3 on the primal-edge sides and h2,h4 on the dual-edge sides. For e∈A, smooth by the noncrossing pairs (h4,h1) and (h2,h3); for e∉A, smooth by (h1,h2) and (h3,h4). Equivalently, these curves are the boundary of NA, formed from disjoint small vertex disks and narrow bands along the edges in A. Define ℓ(A) as the number of connected boundary loops of NA; this also covers a one-vertex graph with no edges. No extra exterior wiring or boundary weight is imposed. Then ZFK(Q,v)=Q∣V∣/2∑A⊆E(Q)ℓ(A)∏e∈AveQ. The loop value is δ=Q. This is a finite mathematical identity; it asserts no semisimplicity, representation faithfulness, trace, categorification, or empirical claim.

Statement references

Facts & Assumptions

Given: A finite connected simple plane graph G, its fixed plane embedding, integer Q≥2, finite activities ve≥0, and free vertices and exterior face.

[F1]

Finite Potts and Fortuin–Kasteleyn weights: GA=(V,A) is spanning, k(A) counts every component including isolated vertices, and ZFK=∑A⊆EQk(A)∏e∈Ave.

[F2]

Plane embeddings of finite simple graphs, their faces, facial boundary walks and lengths (counting a bridge twice), and planar graphs: a plane graph is a finite simple graph with polygonal edge arcs meeting only at shared endpoints; its faces are the regions of the complement.

[F4]

For a plane graph with c components, ∣V∣−∣E∣+∣F∣=1+c, including the null graph: for a plane subgraph with k components, ∣V∣−∣E∣+∣F∣=1+k, including isolated vertices and the null graph.

[F5]

Temperley–Lieb planar diagram data and endomorphism modules: stacking places the left factor above the right factor; each closed cycle removed in a composition contributes the loop value δ.

[F6]

Stacking is associative and horizontal juxtaposition is strict monoidal: these diagram compositions are well-defined and associative for every commutative coefficient ring and every δ.

[F7]

Every connected component of an open subset of Rn is open and polygonally connected (Every connected component of an open subset of Rn is open and polygonally connected).

Proof

technique · direct planar component count
1.1givenF2F3F7construct

Fix A⊆E. The drawing of GA is a finite union of points and closed line segments, hence is closed in R2. Its complement is open, so each of its face components is open and polygonally connected by [F7]. The original faces are vertices of G∗. Two such faces lie in the same face of GA exactly when they are connected by dual edges corresponding to E∖A. A dual path crosses only absent primal edges and therefore stays in the complement of GA. Conversely, a path between two original face points inside one face of GA can be chosen polygonal; perturb its finitely many segments to avoid primal vertices and to cross edge interiors transversely. Because it avoids GA, every crossed edge lies in E∖A, so the crossed dual edges form a path in (F(G),A∗). Thus the faces of GA correspond bijectively to the components counted by k∗(A).

2.1F1F4step 1.1algebra

Apply [F4] to the spanning plane subgraph GA. Its vertex set is V, its edge set is A, its component count is k(A), and by step 1.1 its face count is k∗(A). Therefore ∣V∣−∣A∣+k∗(A)=1+k(A), or k(A)−k∗(A)=∣V∣−∣A∣−1.

2.2F2F3F4F7givenstep 1.1construct

View the fixed embedding on the sphere S2 by adding the point at infinity. By [F4] the plane subgraph GA has finitely many faces; choose one point in each. For every vertex x∈V, including each vertex isolated in GA, choose a closed polygonal disk Dx so that the disks are pairwise disjoint and avoid all the chosen face points. Choose pairwise disjoint attachment intervals on each ∂Dx, one for each edge incident to x and in the cyclic order of the embedding. For each e∈A, choose a closed polygonal rectangular band around the part of its polygonal arc outside its endpoint disks; make the bands pairwise disjoint, disjoint from all nonendpoint disks, and attach them to the corresponding intervals. These choices are possible with sufficiently small radii and widths because the graph is finite and edge arcs meet only at shared endpoints. Let NA be the union of these disks and bands. Each band joins exactly its two endpoint disks, and distinct bands otherwise remain disjoint, so the connected components of NA correspond exactly to those of GA, including isolated-vertex disks; hence there are k(A). Its boundary is a finite disjoint union of simple polygonal circles. In particular, the one-vertex graph with no edges gives one disk and one boundary loop. At a primal-dual crossing, the two sides of an occupied-edge band join the medial half-edges as (h4,h1) and (h2,h3); when the edge is absent, there is no band there and the two face-side sectors join as (h1,h2) and (h3,h4). The cyclic ordering of the attachment intervals fixes these local continuations, so ∂NA gives exactly the smoothings in the Statement and has ℓ(A) loops. Inside each face U of GA, the disk-and-band pieces form collars along the facial boundary side occurrences: each band side is a polygonal strip, and intervening vertex-disk sectors join consecutive strips in cyclic order. Repeated vertices or edges in a facial boundary walk are treated as separate side occurrences, so a bridge's two sides remain distinct; an isolated vertex gives a punctured-disk collar in its unique incident face. Choose all radii and widths small enough that distinct collars meet only at their prescribed continuations within the same face. Trimming them leaves a nonempty core UA=U∖NA, since the selected point of U lies outside NA. The core is connected: for finite points use the polygonal path in the plane face U supplied by [F7], perturb the path to meet the finitely many polygonal collar sides transversely, and replace each excursion into a collar by a parallel path just outside NA along that collar's connected U-facing boundary, with the vertex-disk sector prescribing the continuation at each turn. For the unbounded face, NA is bounded; outside a sufficiently large disk its complement contains the whole exterior, and a ray there joins finite points to the point at infinity in the one-point compactification. Thus the same connected core includes infinity. Each face therefore leaves exactly one nonempty component of S2∖NA. Since NA contains GA, these trimmed faces are all the components of S2∖NA; by step 1.1 their number is k∗(A). The regions of S2∖∂NA are the interiors of the k(A) components of NA together with those k∗(A) complementary components, so there are k(A)+k∗(A) regions.

3.1F4step 2.2constructalgebra

Subdivide each of the disjoint polygonal boundary loops from step 2.2 at at least three distinct points and regard the result as a finite simple plane graph HA, with one cycle component per loop. Each cycle has the same number of vertices and edges, so ∣V(HA)∣=∣E(HA)∣, and HA has ℓ(A) connected components. By [F4], its complement therefore has ∣F(HA)∣=1+ℓ(A) regions. These are the regions counted in step 2.2, hence k(A)+k∗(A)=1+ℓ(A) and ℓ(A)=k(A)+k∗(A)−1.

4.1givenstep 2.1step 3.1algebra

Adding the identities in steps 2.1 and 3.1 yields 2k(A)=∣V∣−∣A∣+ℓ(A). Since Q≥2, its positive square root δ=Q is nonzero, and Qk(A)∏e∈Ave=Q∣V∣/2(Q)ℓ(A)∏e∈AveQ. This equality also holds when an activity is zero, because both corresponding terms vanish.

5.1F1F5F6step 4.1algebra∎

Each subset A selects exactly one of the two noncrossing smoothings at every medial vertex, so summing the termwise identity of step 4.1 over A⊆E gives the displayed loop formula. In any fixed rectangular decomposition, the local smoothings are Temperley–Lieb diagrams; [F5] fixes the left-above-right and δ conventions, and [F6] makes the stacked product independent of parentheses. Thus the local loop weight is exactly δ=Q, without invoking a trace or any representation-theoretic property.

Remarks

The local smoothing convention is shown below. The four ends are listed cyclically; h1,h3 are primal-side half-edges and h2,h4 are dual-side half-edges. The occupied pairing is used when e∈A and the absent pairing when e∉A. The crossing pairing is never used. The proof counts connected components and regions; it does not infer the partition identity from the picture.

h1h2h3h4e2A:(h4;h1);(h2;h3)h1h2h3h4e=2A:(h1;h2);(h3;h4)

The FK connectivity join Ji in the transfer algorithm is an idempotent partition operation. It is not the Temperley–Lieb cup-cap generator, whose square contributes a factor δ.

5 · Examples, experiments and counterexamples

None yet.

Sources