Abstract 1 Introduction 2 Preliminaries 3 Transducing laminar trees References Appendix A Beyond laminar set systems

The Role of Counting Quantifiers in Laminar Set Systems

Rutger Campbell ORCID University of Waterloo, Canada    Noleen Köhler ORCID University of Leeds, UK
Abstract

Laminar set systems consist of non-crossing subsets of a universe with set inclusion essentially corresponding to the descendant relationship of a tree, the so-called laminar tree. Laminar set systems lie at the core of many graph decompositions such as modular decompositions, split decompositions, and bi-join decompositions. We show that from a laminar set system we can obtain the corresponding laminar tree by means of a monadic second order logic (MSO) transduction. This resolves an open question originally asked by Courcelle and is a satisfying resolution as MSO is the natural logic for set systems and is sufficient to define the property “laminar”. Using results from Campbell et al. [STACS 2025], we can now obtain transductions for obtaining modular decompositions, co-trees, split decompositions and bi-join decompositions using MSO instead of CMSO. We further gain some insight into the expressive power of counting quantifiers and provide some results towards determining when counting quantifiers can be simulated in MSO in laminar set systems and when they cannot.

Keywords and phrases:
MSO-transductions, simulating counting quantifiers, laminar set systems, graph decompositions
Category:
Track B: Automata, Logic, Semantics, and Theory of Programming
Funding:
Rutger Campbell: Campbell was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (No. RS-2025-00563533).
Copyright and License:
[Uncaptioned image] © Rutger Campbell and Noleen Köhler; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Theory of computation Finite Model Theory
Related Version:
Full Version: https://arxiv.org/abs/2512.02617 [6]
Acknowledgements:
We thank the Institute for Basic Science (IBS-R029-C1) for funding and hosting the research visit where this work was conducted.
Editors:
Sayan Bhattacharya, Danupon Nanongkai, Michael Benedikt, and Gabriele Puppis

1 Introduction

A transduction transforms relational structures over an input vocabulary Σ1 into relation structures over an output vocabulary Σ2 in such a way that each Σ2-definable property can be “translated” back to a Σ1-definable preimage. For example, given a rooted tree, we can define a predicate Φ𝖲𝖤𝖳(S) that is true for a set S when it consists of all the leaves that are the descendents of a node of the tree. We can use this to construct a transduction that takes a tree and outputs a set system on the leaves of a tree; the predicate Φ𝖲𝖤𝖳(S) is true when S is in this “laminar” set system, which we define below.

Theorem 1 (Backwards Translation Theorem [14, Theorem 1.40]).

Let τ be a MSO-transduction with input vocabulary Σ1 and output vocabulary Σ2. If ϕ2 is an MSO-sentence over Σ2, then there is a MSO-sentence ϕ1 over Σ1 so that, the sentence ϕ1 holds for precisely the structures 𝔸 over Σ1 for which τ(𝔸) contains a structure satisfying ϕ2.

This is incredibly useful as it means derived structure can be used to define properties. Of particular significance is derived tree-structure, due to connections to tree-automata. Thatcher and Wright showed that a property of labelled rooted trees of bounded degree is MSO-definable if and only if it is recognizable by a tree-automaton. So if we have a class of objects from which we can transduce a bounded tree description, from whence we can, in turn, transduce the original object, then MSO-definability of the original object corresponds to recognizability with a tree-automaton. Most prominently, for graphs of bounded treewidth, MSO-definability is equivalent to tree-automaton recognizability, where the forwards implication is Courcelle’s Theorem [8] and the backward direction was finally shown by Bojańczyk and Pilipczuk in 2016 [3]. Using the powerful method of Simon’s factorization [19], results of Bojańczyk et al. [2] show that definability equal recognizability in graphs of bounded linear clique-width. This was recently generalized to show definability equal recognizability in classes of represented matroids [1] and more generally representable matroids [5] over finite fields of bounded linear branch-width. In pursuit of understanding when definability equals recognizability, Courcelle studied how to transduce tree-structured decompositions such as modular decompositions and split decompositions [9, 10, 11, 13]. In this series of work, Courcelle uses order invariant MSO, which allows the use of a global linear order of the vertices of the graph. This logic is strictly more expressive than MSO with counting quantifiers, denoted CMSO, which in turn is more expressive than MSO [17]. These transductions were recently improved to avoid the use of linear order, but still relied on counting quantifiers for the key step [4]. This key step involves transducing a tree from a collection of sets and was independently described by Bojańczyk [1], also using counting quantifiers. It was asked as an open question in [1, 4, 12] whether this transduction can be obtained using just MSO.

We focus on transducing a tree from the setting of set systems as it provides a general form of structure itself, to which many other settings have a transduction to. A set system is a pair (U,) consisting of a set U and a family of subsets of U. We model this over the vocabulary {𝖲𝖤𝖳}, consisting of the unary set-predicate 𝖲𝖤𝖳, by taking {𝖲𝖤𝖳}-structure 𝔽 with universe U𝔽 and taking 𝖲𝖤𝖳𝔽(X) to be true precisely when X. We say a set system is laminar when: U, for each uU the singleton {u}, and for any F1,F2 the intersection F1F2 is one of ,F1,F2. For the last condition, we say that F1 and F2 do not cross.

Any laminar set system (U,) naturally corresponds to a tree T, called the laminar tree of (U,), in which each node of T corresponds to a set in and the descendant relation coincides with the subset relation. In particular, U corresponds to the root of T while the leaves of T correspond to the singletons {u}. For ease of notation, we identify uU with the leaf that corresponds to the singleton {u}. Doing this, a set F corresponds to the node vF of T where the leaves that are descendants of vF are precisely the elements of F. For an example of a laminar set system and its laminar tree see Figure 1.

In this paper, we give an MSO-transduction that, given any laminar set system (U,), outputs its laminar tree. Observe that the tree structure of the laminar tree is implicitly already given by the subset relationship. But in the set system language we do not have any means of talking about the nodes of the laminar tree, and can only specify sets of leaves. Indeed, the tricky part is to identify elements that can play the role of each node of the laminar tree T. We introduce a new technique for choosing a representative leaf for every node of a tree in such a way that each leaf is used only a bounded number of times which is the key step in our transduction. Formally, our main theorem is the following:

Theorem 2.

There is an MSO-transduction τ from structures over the vocabulary {𝖲𝖤𝖳} of set systems, to structures over the vocabulary {𝖽𝖾𝗌𝖼} of rooted trees, such that if (U,) is a laminar set system, then every τ-image of (U,) is isomorphic to the laminar tree of (U,) as a 𝖽𝖾𝗌𝖼-structure.

Figure 1: An example of a laminar set system and its laminar tree.

We present two consequences of this theorem. Firstly, we gain some insights into when CMSO for laminar set systems is strictly more expressive than MSO and when they are equivalent.

Theorem 3.

Let k be a positive integer. Let k be the collection of laminar set systems whose laminar trees have down-degree at most k. Then there is a unary set-predicate EVENk, that is {𝖲𝖤𝖳}-definable over MSO, where for any set system (U,)k and any XU, we have EVENk(X) as true if |X| is even.

By adapting a known result of non-definability in MSO, we give the following partial converse to Theorem 3.

Theorem 4.

If contains laminar set systems whose laminar trees are stars of unbounded degree, then we cannot {𝖲𝖤𝖳}-define EVEN for over MSO.

Both Theorem 3 and Theorem 4 can be shown using standard techniques and we therefore do not include a proof. For details please refer to the preprint [6].

Secondly, Theorem 2 has numerous corollaries in various graph theory decompositions. This work is a strengthening of Theorem 2 from [4], which provides a transduction from laminar set systems to laminar trees using MSO with modulo counting quantifiers. Our result shows that we can achieve the same transduction without the use of a parity predicate. So by combining Theorem 2 with the other transductions given in [4] (see [4, Theorem 1]), we obtain similar corollaries, but now without the use of counting predicates.

Weakly-partitive set systems are less restrictive than laminar set systems. In a weakly-partitive set system, two sets F1 and F2 in the family can cross but only if F1F2,F1F2,F2F1 and F1F2 are also in the family. For example, in graph theory the set of modules of a directed graph is weakly-partitive. The “weakly-partitive tree” of a weakly-partitive set system is obtained from the laminar tree T of a particular laminar sub-set-system (the sets that do not cross any other), by adding a node labelling λ and a partial ordering < in a way that fully capture the structure of the weakly-partitive set system (see [7]). More specifically, λ:V(T){𝗉𝗋𝗂𝗆𝖾,𝖽𝖾𝗀𝖾𝗇𝖾𝗋𝖺𝗍𝖾,𝗅𝗂𝗇𝖾𝖺𝗋} such that for every inner node tV(T) with children s0,,s the following holds:

  • if λ(t)=𝗉𝗋𝗂𝗆𝖾 then the set of leaves below t forms a set in the set system but for no set I[] with 2|I|< the set of leaves below either of the si, iI is in the set system.

  • if λ(t)=𝖽𝖾𝗀𝖾𝗇𝖾𝗋𝖺𝗍𝖾 then the set of leaves below t forms a set in the set system and for every set I[] the set of leaves below either of the si, iI is in the set system.

  • if λ(t)=𝗅𝗂𝗇𝖾𝖺𝗋 then the set of leaves below t forms a set in the set system. It further must hold that s0<<s and for a set I[] the set of leaves below either of the si, iI is in the set system exactly when I forms a <-interval.

Furthermore, every set in the bipartitive set system is of this form. We obtain the following transduction.

Corollary 5.

There is an MSO-transductions τ such that given any weakly-partitive set system (U,𝒮) modelled by the {𝖲𝖤𝖳}-structure 𝕊, τ outputs the weakly-partitive tree (T,λ,<) of (U,𝒮).

Instead of considering set systems, we can also consider systems of bi-partitions. Systems of bi-partitions naturally model concepts in graph theory such as splits and bi-joins. The concept analogous to weak-partitiveness in the world of systems of bipartitions is weak-bipartitiveness. We obtain a similar corollary which allows one to MSO-transduce a tree and additional structure that captures the bipartitions of a weakly-bipartitive system.

Corollary 6.

There is an MSO-transductions τ such that, given any weakly-bipartitive system of bipartitions (U,) modelled by the {𝖡𝖨𝖯𝖠𝖱𝖳}-structure 𝔹, τ outputs the weakly-bipartitive tree (T,λ,<) of (U,).

Finally, we can apply Theorem 2 and the machinery from [4] to obtain transductions for the graph decompositions mentioned above whose underlying set system or system of bipartitions are weakly-partitive or weakly-bipartitive, repectively.

Corollary 7.

There are non-deterministic MSO-transductions τ1,,τ4 such that:

  1. 1.

    Given any (directed) graph G as input, τ1 outputs the modular decomposition (T,F) of G.

  2. 2.

    Given any (directed) cograph G, τ2 outputs the cotree (T,λ) of G.

  3. 3.

    Given any (directed) graph G, τ3 outputs a split decomposition (T,F) of G.

  4. 4.

    Given any graph G, τ4 outputs a bi-join decomposition (T,F) of G.

In the Appendix, we discuss in further detail how to modify the proofs from [4] to obtain Corollaries 5, 6, and 7.

We believe that this line of research of considering transductions for simple, but dense tree-structured decompositions is important in the context of definability-equals-recognizability results. Notably, there are such results for trees [21] and graphs of bounded treewidth [3] which we can think of as sparse tree-structured decompositions. Additionally, there are definability-equals-recognizability results for dense graph classes in the case the decomposition used has a linear structure [2, 5]. It is open, however, whether in graphs of bounded clique-width, for which [15] yields one direction, whether definability equals recognizability. In some sense, our results solve a restricted version of this major open problem.

2 Preliminaries

We denote by [n] for n the set [n]={1,,n}. A partition (P1,,P) of a set U has to satisfy that the parts Pi are pairwise disjoint and their union is U but we do not require parts to be non-empty. For a partition (P1,,P) a refinement of (P1,,P) is a partition (S1,,Sk) such that each SiPj for some j[].

We consider trees to be rooted and, therefore, equipped with an ancestor-descendant relationship. We use the usual terminology regarding parent, child and sibling. We distinguish between ancestors/descendants of a node t that include t itself, and proper ancestors/proper descendants of t that are all ancestors/descendants except t. In addition, for a tree T we denote the set of nodes by V(T) and the set of edges of T by E(T). For a tree T nodes of degree 1 are called leaves of T. Each node in T that is not a leaf, we call an inner node. For a tree T and a node tV(T), we denote by Tt the subtree of T induced by all descendants of t.

2.1 Logic and transductions

In order to model set systems we consider extended relational structures which extend the usual notion of relational structures to allow the use of set predicates. An extended relational vocabulary is a set Σ of symbols each having an associated arity, denoted ar(Q) for QΣ, where each symbol is either a relation name or a set predicate name. An extended relational structure over Σ, or short Σ-structure, is a tuple 𝔸=(U𝔸,(Q𝔸)QΣ) consisting of a finite set of elements U𝒜, called the universe of 𝔸, and an interpretation Q𝔸 of every QΣ where Q𝔸 is a relation over U𝔸 if Q is a relational name and a relation over subsets of Q𝔸 if Q is a set predicate name. For two vocabularies Σ and Γ, and a Σ-structure 𝔸 and a Γ-structure 𝔹 we let the union, denoted 𝔸𝔹, be the ΣΓ-structure with universe U𝔸U𝔹 and the interpretation Q𝔸𝔹 of some symbol QΣΓ being either Q𝔸, Q𝔹 or Q𝔸Q𝔹 depending on whether Q belongs to ΣΓ, to ΓΣ, or to ΣΓ. For a Σ-structure 𝔸 and a Γ-structure 𝔹, we write 𝔸𝔹 if ΣΓ, U𝔸U𝔹 and for each symbol Q in Σ, Q𝔸=Q𝔹.

We define syntax and semantic of monadic second order logic, short MSO, in the usual way and refer, for example, to [14, 16, 18, 20] for the definition of MSO on extended relational structures. MSO extends first-order logic introducing set variables and allowing quantification over set variables. To clearly identify the type of variables, we distinguish between element variables and set variables and use lowercase letters, such as r,x,y,z,, for element variables and uppercase letters, such as R,X,Y,Z,, for set variables. For a formula ϕ, we write ϕ(x1,,x,X1,,Xk) to indicate that the free element variables of ϕ are x1,,x and the free set variables of ϕ are X1,,Xk, namely, the set of variables occurring in ϕ that are not bound to a quantifier within ϕ. A sentence is a formula without any free variables. Where possible, we follow the convention that relational names and formulas with only free element variables are lowercase while set predicate names as well as formulas that have only free set variables receive uppercase names.

We use the following extended relational structures to model the structures used in this paper. We use vocabulary {𝖲𝖤𝖳} where set is a set predicate name of arity 1 to model set systems. Naturally a set system (U,) is modelled by the {𝖲𝖤𝖳}-structure 𝔽 with universe U=U and for which is the interpretation 𝖲𝖤𝖳𝔽. Additionally, we use vocabulary {𝖣𝖤𝖲𝖢} to model trees. Here a tree T is modelled by the {𝖣𝖤𝖲𝖢}-structure 𝕋 with universe U𝕋=V(T) and the interpretation 𝖣𝖤𝖲𝖢𝕋 being the descendant relationship of T. Note that choosing the descendant relationship is arbitrary and the relations 𝖠𝖭𝖢,𝖢𝖧𝖨𝖫𝖣 and 𝖯𝖠𝖱𝖤𝖭𝖳 can easily be defined in MSO.

We note that MSO is sufficient to define laminar sets systems. Indeed, laminar set systems are exactly those that satisfy the {𝖲𝖤𝖳}-formula

(Ux(xU𝖲𝖤𝖳(U)))(xSy(𝖲𝖤𝖳(S)[ySy=x]))
(F1F2(𝖲𝖤𝖳(F1)𝖲𝖤𝖳(F2)[F1F2=F1F2=F1F1F2=F2])).

Transductions

Let Σ and Γ be two extended relational vocabularies. A Σ-to-Γ transduction is a set τ of pairs formed by a Σ-structure, call the input, and a Γ-structure, called the output. We write 𝔹τ(𝔸) when (𝔸,𝔹)τ. When for every pair (𝔸,𝔹)τ we have 𝔸𝔹, we call τ an overlay transduction. Transductions are defined over some logic , such as FO, MSO or CMSO. In this paper the ambient logic is MSO and we therefore restrict the following definitions to this case. We define an MSO-transduction to be a transduction obtained by composing a finite number of atomic MSO-transductions of the following kinds.

Colouring

A k-colouring tansduction adds k new unary predicates 𝖼𝗈𝗅𝗈𝗎𝗋i for every i[k] to the signature Σ while adding k new unary relations to the original Σ-structure 𝒜 interpreting the k additional predicates. Any possible interpretation yields an output of the transduction. Hence, it defines a total (non-functional) relation from Σ-structures to Γ-structures where Γ=Σ{𝖼𝗈𝗅𝗈𝗎𝗋ii[k]}.

Copying

A k-copying transduction adds k copies of the universe of the original Σ-structure 𝔸 to 𝔸 while introducing new binary predicates 𝖼𝗈𝗉𝗒i(x,y) for every i[k] which expresses that element x is the i-th copy of the original element y. Hence, this defines a function from Σ-structures to Γ-structures, where Γ=Σ{𝖼𝗈𝗉𝗒ii[k]}. For sake of easier notation, we assume that a binary predicate 𝖼𝗈𝗉𝗒0(x,y) is added which is satisfied for elements x,y if x is an original element and y=x (y is the original copy of x).

Filtering

A filtering transduction is specified by a sentence χ over Σ and outputs the original Σ-structure 𝔸 if 𝔸 satisfies χ. Hence, a filtering transduction defines a partial function from Σ-structures to Σ-structures.

Interpretation

An interpretation is a transduction from Σ to Γ-structure for any signature Σ and Γ. An interpretation is specified by a tuple (ϕ,(ψQ)QΓ) where ϕ is a formula over Σ with one free variable and ψQ for any QΓ is a formula over Σ with ar(Q) free variables (which may be set variables if Q is a set predicate). For input Σ-structure 𝔸 the interpretation returns the Γ-structure whose universe consists of all elements of the universe of 𝔸 satisfying ϕ and for which Q is interpreted by the set of all tuples satisfying ψQ for each QΓ. This defines a function from Σ-structures to Γ-structures.

We often refer to unary predicates as colours and therefore we describe k-colouring transductions as a colouring of the universe with k colours. Note that in such a colouring any element might receive multiple colours or even no colour at all.

In the following we show that we can easily obtain laminar set systems from their laminar trees by means of a transduction.

Lemma 8.

For any laminar set system (U,) with laminar tree T modelled by the {𝖣𝖤𝖲𝖢}-structure 𝕋 there is an MSO-transduction τ which on input 𝕋 produces the {𝖲𝖤𝖳}-structure 𝔽 that models (U,).

Proof.

We can transduce the set system from the laminar tree using a single MSO-interpretation. We obtain the {𝖲𝖤𝖳}-structure 𝔽 modelling (U,) from 𝕋 by restricting the universe consisting of all nodes of T to just the leaves of T. Additionally, we can define the predicate 𝖲𝖤𝖳 by a formula ψ𝖲𝖤𝖳(X) expressing that X is the set of leaves of the subtree Tt of T rooted at t for some node tV(T). Specifically, we can implement this interpretation as follows:

ϕ(x) :=y(𝖣𝖤𝖲𝖢(y,x)y=x), and
ψ𝖲𝖤𝖳(X) :=yx(𝖣𝖤𝖲𝖢(x,y)xX).

Note by the Backwards Translation Theorem (Theorem 1), this means that any property of laminar set system that can be defined in the language of sets corresponds to a property of rooted trees definable in the language of rooted trees.

3 Transducing laminar trees

In this section we prove our main result, which consists in constructing a transduction that, given a laminar set system (U,) outputs its laminar tree T.

Our first task is to choose an elements of the set system for every inner node. This element will play the role of the node in the tree structure. However, we need to guarantee that every element plays the role of at most a bounded number of inner nodes (in our case this bound is 17) and we can identify which element plays the role of a particular node in MSO. In order to achieve this, we partition the nodes of T into 17 sets. For each of these parts, we will use a copy of the universe U to contain elements to play the roles of the nodes in this part. We will call the element (in a copy of U) that plays the role of some node t of T the “realization” of the set F𝔽 corresponding to the node t. Any copy of an element can be the realization of at most one set. One of the 17 parts will contain exactly the leaves of the tree T for which it is trivial to identify realizations. All other parts of the partition consist of a carefully chosen set of inner nodes. From now on, assume that S is one of the parts containing only inner nodes of T. To find realizations for all nodes in S, we first choose a subtree of T for each element sS, denoted Hs. We show that we can choose such trees to be pairwise disjoint if we choose S to be thin. Thinness roughly enforces that we can always grow the representative tree of a node tS downwards in such a way that avoids S itself, which is crucial for guaranteeing disjointness of representative trees.

Besides choosing the representative trees disjoint, we also enforce them to have a particular structure. This structure allows us, given all the leaves of representative trees in S to pinpoint (by means of an MSO-formula) exactly which leaves belong to the same representative tree. Hence, we can use a colouring-transduction to identify all possible choices of the leaves of the representative trees of nodes in S. We then filter which colourings correctly correspond to the set of leaves of representative trees. We chose an arbitrary leaf in the set of leaves of the representative tree of a node tS to be the realization of the set F corresponding to t. Distinctness trivially follows from the disjointness of the representative trees. We then interpret, restricting the universe to only contain realizations of sets and introducing the descendant relationship through the subset relationship.

3.1 Representative sets

Let (U,) be a laminar set system, T be the laminar tree of (U,) and L the set of leaves of T. We equip T with a labelling δT:V(T){0,1,2,3} where a node of depth d in T is assigned label dmod4.

Let s be a node of T. A representative tree of s is a subtree H of T with the following properties.

(RT1)

s is the root of H.

(RT2)

If δT(s)δT(t)mod2 for some tV(H), then either t is a leaf of T or H contains exactly one child of t.

(RT3)

If δT(s)δT(t)mod2 for some tV(H), then either t is a leaf of T or H contains every child of t.

For a set BL we call the parent of the least common ancestor of the leaves in B the tip of B. We call the minimum subtree of T which contains both B and the tip of B the up-tree of B, denoted HB. A representative set of a node s of T is a set As of leaves of T for which s is the tip and the up-tree HAs of As is a representative tree.

A set SV(T)L of inner nodes of T is called thin if there is i{0,1,2,3} such that the following properties hold.

(TS1)

δT(s)=i for every sS.

(TS2)

Every sS has a sibling which is not in S with the exception of the root of T.

(TS3)

The parent ts of every sS has a sibling ts such that no child of ts is in S with the exception of nodes sS that are children of the root of T.

We first prove that thinness is enough to choose disjoint representative trees for all nodes of the thin set. For this, the following observation is an important ingredient.

Observation 9.

In the laminar tree T of any laminar set system (U,) every inner node has at least two children.

Note that for the following lemma property (TS2) is not needed. We, however, require this property later.

Figure 2: Part of a laminar tree with thin set S consisting of the larger orange nodes and pairwise disjoint representative trees of the nodes in S highlighted in blue.
Lemma 10.

If SV(T)L is thin, then we can choose representative sets (As)sS such that the representative trees HAs, sS are pairwise disjoint.

Proof.

Let SV(T)L be a thin set and let i{0,1,2,3} be the index such that δT(s)=i for every sS which exists by (TS1). Let P be the set of all nodes which are parents of a node in S. Note that every pP satisfies δT(p)i1mod4. We show that for every sS we can choose a representative tree Hs, which avoids all nodes in P (i.e. does not contain any node in P). This statement implies that the representative trees Hs are pairwise disjoint. Clearly, since Hs is a subtree of Ts for every sS by (RT1), Hs and Hs must be disjoint whenever s,sS are not in any ancestor-descendant relationship. If, on the other hand, sS is a proper ancestor of sS then Hs cannot intersect Ts (and hence cannot intersect Hs) since Hs avoids the parent of s.

We now describe how to construct Hs which avoids P for some fixed sS. We construct the tree Hs recursively, level by level. The 0-th level of Hs consists of just s, which clearly avoids P. Assume that we have constructed j levels of Hs that avoid P and aim to construct the (j+1)-th level. We distinguish the following cases. First, assume jimod4. In this case, for every node t on level j of Hs, which is not a leaf, we choose an arbitrary child t of t and add it to Hs. Note that the updated tree Hs clearly still avoids P as δT(t)i+1mod4 for every newly added node t while nodes pP satisfy δT(p)i1mod4. Next, assume that ji+1mod4. In this case, for every t on level j of Hs, which is not a leaf of T, we add all children of t to Hs. Again, each newly added vertex t cannot be contained in P as they satisfy δ(t)i+2mod4. Next, we assume that ji+2mod4. In this case, for every node t on level j of Hs, which is not a leaf, we choose a child t of t which is not in P and add it to Hs. This choice is always possible, as S is thin and, therefore, every node pP must have a sibling that does not have children in S by (TS3) which implies that this child is not contained in P. Clearly, the updated tree Hs avoids P. Finally, assume ji+3mod4. For every node t on level j of Hs, which is not a leaf, we add all its children to Hs. The resulting tree Hs still must avoid P as each newly added node t satisfies δT(t)=i while nodes pP satisfy δT(p)i1mod4. Note that the tree Hs is a representative tree of s by construction because s is the root of Hs implying (RT1), for every node t on level j with jimod2 (unless t is a leaf) we added exactly one child of t to Hs implying (RT2) and for every node t on level j with jimod2 (unless t is a leaf) we added all children of t to Hs implying (RT3). For an illustration of the construction of representative trees of a thin set S we refer the reader to Figure 2.

We can now set As to be the leaves of Hs for every sS. We argue that HAs=Hs. First note that because s is the root of Hs the least common ancestor of the leaves in As is some descendant of s. By construction, there is exactly one child t of s which is contained in Hs and therefore the least common ancestor of the leaves in As is indeed a descendant of t. Let t1,,t be the children of t and note that 2 by Observation 9. By construction t1,,t are contained in Hs (as δ(t)i+1mod4). Since both the subtree of Hs rooted at t1 and the subtree of Hs rooted at t2 must have a leaf and therefore an element of As, the least common ancestor of the leaves in As is t. Therefore the tip of As is s. Hence, by definition of up-tree, we know that HAs is a subtree of Hs as Hs contains all nodes in As and the tip of As. Furthermore, every node t contained in Hs must be in HAs because the subtree of Hs rooted at t must contain a leaf t which, by construction, is in As. Then the path from t to s must be contained in HAs implying that t is in HAs. Since HAs=Hs is a representative tree, As is a representative set. Hence, we have chosen representative sets (As)sS with pairwise disjoint representative trees HAs proving the statement.

We now aim to prove that given the union A:=sSAs of representative sets of a thin set, the representative sets are uniquely defined. We further aim to characterize representative sets in such a way that we can later define them in MSO. For this we use the following terminology. Given a set B of leaves of T we say that an inner-node sV(T)L is fully-branched (by B) if for every child t of s the set of leaves in the subtree of T with root t contains a leaf from B. Additionally, we say that s is single-branched (by B) if there is exactly one child t of s for which the subtree of T rooted at t contains a leaf from B. We say that s is missed (by B) if the subtree of T rooted at s contains no leaves from B.

Lemma 11.

Let S be a thin set, (As)sS representative sets with pairwise disjoint up-trees HAs and A:=sSAs. For any BL and any node s of T, B=As if and only if all of the following hold:

  1. (i)

    BA,

  2. (ii)

    s is the tip of B,

  3. (iii)

    if a descendant t of s is fully-branched by B, then each child of t is either a node single-branched by B or a leaf in B,

  4. (iv)

    if a descendant t of s is single-branched by B then exactly one child of t is either a node fully-branched by B or a leaf in B, while all other children of t are either missed by B or leaves not in B, and

  5. (v)

    there is no proper superset B of B and node s of T such that B together with s satisfy properties (i)-(v).

Proof.

We start by proving parts of the forwards direction of the statement.

Claim 12.

For every sS the representative set As and node s satisfy conditions (i)-(iv).

Proof.

First, note that condition (i) is trivially satisfied. Additionally, (ii) is satisfied by the definition of representative sets. By definition HAs is a representative tree. Observe that for any node t of HAs there is some leaf v of Tt that is contained in HAs by the definition of representative trees. In particular, this implies that every node t in HAs with δT(t)δT(s)mod2 is either a leaf of T or single-branched by As and every node t in HAs with δT(t)δT(s)mod2 is either a leaf of T or fully-branched. Since every inner node t not contained in HAs is missed by As, we obtain properties (iii)-(iv). The essential property we use to prove the equivalence in the statement of the lemma is that the up-tree HB of any set B satisfying the properties (i)-(iv) together with the tip of B must be a subtree of HAs for some sS. We prove this statement in two steps Claim 13 and Claim 14. Let F be the forest obtained by taking the union of HAs for all sS.

Claim 13.

It holds that F is an induced subgraph of T with connected components HAs, sS.

Proof.

Let i{0,,3} such that i=δT(s) for every sS (note that i is well defined as S is thin). First note that by assumption the subtrees HAs are pairwise disjoint and therefore are the connected components of F. Towards a contradiction assume the claim is not true and let u,sV(F) be nodes of T such that s is the child of u in T, but the edge us is not in E(F). In this case s is the root of some tree of F and in particular sS. Additionally, u is a node in some tree Hu with uS and us. In particular, δT(s)=δT(u)=i. Since u is the parent of s this implies that δT(u)δT(u)mod2. Since additionally Hu is a representative tree, all children of u must be contained in Hu. This contradicts the disjointness of representative trees, since s is a child of u and is contained in Hs.

Claim 14.

For any set B with tip s such that B and s satisfy properties (i)-(iv) the tree HB does not contain any node which is not contained in F.

Proof.

Our argument essentially amounts to showing that there are not enough leaves from A in any subtree of T with root in V(HB)V(F) in order for B to satisfy properties (i)-(iv). See Figure 3 for an illustration. To argue this formally, let t be a node which is contained in HB but not in F and has maximum depth with this property. Let (p0,p1,,p) with p0=s and p=t be the path in HB from s to t. First observe that no node of p0,,p can be missed by B as they are contained in HB which implies that at least one descendant must be contained in B for each of p0,,p. Property (ii) implies that p0=s is single-branched by B. By property (iv) and since p1 cannot be missed or be a leaf, we conclude that p1 is fully-branched. By property (iii) and because p2 cannot be a leaf, this implies that p2 is single-branched by B. Using a recursive argument, we conclude that pi must be fully-branched by B if i is odd and pi is single-branched by B if i is even for every i{0,,}.

We can further argue the following property:

() For every leaf uAtB it holds that t is a child of t.
Figure 3: The different cases to obtain a contradiction in Claim 14. Here larger orange diamonds are nodes in S, representative trees are highlighted in blue (in particular F is blue), parts of the tree belonging to HB are highlighted in red and the red squares are leaves in B.

To see this, observe that for any node tS and uAtB the path between t and u must be fully contained in F. Since t is not contained in F, we conclude that t must be a proper descendant of t. Now, let tS be any node that is contained in the subtree (HB)t of HB rooted at t. By Claim 13 we have that HAt is a connected component of F. Since F is an induced subgraph of T by Claim 13, this implies that the parent t′′ of t cannot be contained in F. On the other hand, t′′ must be contained in HB as t is in the tree HB and t is an ancestor of t′′. Since we choose t of maximum depth with the property of being in HB but not in F this implies that t′′=t. Therefore, every node in (HB)t that is contained in S must be a child of t and hence property () holds. We now distinguish two cases.

First assume that is odd and therefore t=p is fully-branched (see right two part of Figure 3). Hence, every child of t must be the ancestor of a node in BA. By property (), every child of t must be contained in S. But this contradicts the fact that S is thin.

On the other hand, assume is even and, therefore, t=p is single-branched (see left part of Figure 3). Let t be the child of t that is either a leaf or fully branched, and which exists due to (iv). By statement (), we know that tS and therefore cannot be a leaf as S only contains inner nodes of T. Because tS we know that HAt is a representative tree and therefore t has only one child contained in HAt by (RT2). By Observation 9 t has at least two children and hence there is a child t′′ of t such that Tt′′ contains no leaves from At. On the other hand, by (iv) t is fully-branched by B (as t is single-branched) and therefore there must be a leaf uBA in the subtree Tt′′. In particular, uAt. But this contradicts the property () as t is the child of t that is an ancestor of u. Using the above claims we can now conclude the proof as follows. First assume B=As for some sS. By Claim 12, B and s satisfy (i)-(iv). Presume that B does not satisfy (v) and BB and node s satisfy properties (i)-(iv). Combining Claim 14 and Claim 13, we know that HB must be a subtree of a connected component of F. More specifically, HB must be a subtree of HB=HAs. This implies that BB which contradicts the assumption that B is a proper superset of B.

On the other hand, assume BA and node s satisfy conditions (i)-(iv). By Claim 14 and Claim 13, HB is a subtree of a connected component of F which means that HB is a subtree of HAs for some sS. In particular, this implies that BAs. Finally, B must in fact be equal to As because otherwise As is a proper superset of B which together with s satisfies properties (i)-(iv) by Claim 12. Additionally, s=s because s is the tip of B while s is the tip of As=B and hence B=As.

3.2 The colouring

In this subsection, we describe the colouring we use for our transduction and introduce the predicate we obtain that identifies representative sets for all inner nodes. For this, we first show that we can partition the nodes of every laminar tree into a finite number of thin sets.

Lemma 15.

Let T be the laminar tree of a set system (U,) and L the set of leaves of T. We can partition the set of inner nodes V(T)L into 16 (possibly empty) thin sets.

Proof.

For every i{0,1,2,3}, let ViV(T)L be the set of inner nodes with δT(t)=i. We further denote the root of T by r and the children of r by s1,,s. We show that for every i{0,1,2,3} we can partition Vi into 4 thin sets.

Hence fix i{0,1,2,3} and let j1i1mod4 and j2i2mod4. We first partition Vi into two sets P𝖫𝖾𝖿𝗍,P𝖱𝗂𝗀𝗁𝗍 as follows. For every node tVj1 (these are the parents of nodes in Vi) consider the children t1,,tk of t which are contained in Vi. Note that every child of t is either a leaf of T or contained in Vi. We add t1 to P𝖫𝖾𝖿𝗍 and t2,,tk to P𝖱𝗂𝗀𝗁𝗍. In case t has only one child in Vi (all other children are leaves of T), no node gets added to P𝖱𝗂𝗀𝗁𝗍 and in case t has no children in Vi (all children of t are leaves of T), neither P𝖫𝖾𝖿𝗍 nor P𝖱𝗂𝗀𝗁𝗍 receive an additional node. We further add r to P𝖫𝖾𝖿𝗍 if i=0 (r is the only node in Vi with no parent in Vj1). By construction, the partition (P𝖫𝖾𝖿𝗍,P𝖱𝗂𝗀𝗁𝗍) of Vi has the property that every node in P𝖫𝖾𝖿𝗍 has a sibling which is not contained in P𝖫𝖾𝖿𝗍 with the exception of r in case i=0 and the same holds for P𝖱𝗂𝗀𝗁𝗍. For this we crucially rely on Observation 9 and additionally observe that the sibling of some node tP𝖫𝖾𝖿𝗍, which is not contained in P𝖫𝖾𝖿𝗍, can either be a leaf or contained in P𝖱𝗂𝗀𝗁𝗍 and similarly for P𝖱𝗂𝗀𝗁𝗍.

We now refine the partition (P𝖫𝖾𝖿𝗍,P𝖱𝗂𝗀𝗁𝗍) of Vi into a partition (S𝖫𝖫,S𝖫𝖱,S𝖱𝖫,S𝖱𝖱) as follows. For every node t in Vj2 (these are grandparents of nodes in Vi) consider the children t1,,tk of t that have at least one child in Vi (k could be 0 if all children of t are either leaves or only have leaves as children). For t1 we put all children contained in P𝖫𝖾𝖿𝗍 into S𝖫𝖫 and all children contained in P𝖱𝗂𝗀𝗁𝗍 into S𝖫𝖱. Similarly, we put all children of t2,,tk which are contained in P𝖫𝖾𝖿𝗍 into S𝖱𝖫 and all children of t2,,tk that are contained in P𝖱𝗂𝗀𝗁𝗍 into S𝖱𝖱. Finally, r gets added to S𝖫𝖫 in case i=0 and all children among s1,,s of s contained in P𝖫𝖾𝖿𝗍 get added to S𝖫𝖫 while all children of s contained in P𝖱𝗂𝗀𝗁𝗍 get added to S𝖫𝖱 in case i=1 (s,s1,,s are all the nodes in Vi that cannot have a grandparent in Vj2). For an illustration of the construction, see Figure 4. We now argue that each set of the partition is thin.

Clearly (S𝖫𝖫,S𝖫𝖱,S𝖱𝖫,S𝖱𝖱) is a refinement of (P𝖫𝖾𝖿𝗍,P𝖱𝗂𝗀𝗁𝗍) and therefore still satisfies the condition that every node in S has a sibling that is not contained in S, with the exception of rS𝖫𝖫 in the case i=0. Hence, S𝖫𝖫,S𝖫𝖱,S𝖱𝖫 and S𝖱𝖱 each satisfy condition (TS2). Furthermore, by construction, we also satisfy that the parent pt of every node tS𝖫𝖫 has a sibling which is not contained in S𝖫𝖫 with the exception of r and s1,,s and the same holds for S𝖫𝖱,S𝖱𝖫 and S𝖱𝖱. To see this, consider tS𝖫𝖫S𝖫𝖱, the parent pt of t and the grandparent qt of t. By Observation 9 qt must have an additional child ppt. This additional child p is either a leaf (in which case none of p’s children are contained in S𝖫𝖫 or S𝖫𝖱) or by construction all non-leaf children of p are contained in S𝖱𝖫 and S𝖱𝖱. Now consider tS𝖱𝖫S𝖱𝖱, the parent pt of t and the grandparent qt of t. By construction all non-leaf children of t are in S𝖱𝖫S𝖱𝖱. Therefore, by construction, qt must have a child pqt (the “first” child in our construction) for which all non-leaf children are contained in S𝖫𝖫S𝖫𝖱. Hence, each part S𝖫𝖫,S𝖫𝖱,S𝖱𝖫,S𝖱𝖱 also satisfies (TS3). Finally, (TS1) is trivially satisfied for each part S𝖫𝖫,S𝖫𝖱,S𝖱𝖫,S𝖱𝖱 and they are therefore thin sets.

Figure 4: A partition of the inner nodes of a laminar tree into thin sets where for i=2 the partition (S𝖫𝖫,S𝖫𝖱,S𝖱𝖫,S𝖱𝖱) of Vi is highlighted in colour and the other parts of the partition into thin sets are hinted at using different shapes and shades of gray.

Let (U,) be a set system and T the laminar tree of (U,) where we denote the set of leaves of T by L. By Lemma 15 we can partition the inner nodes V(T)L of T into at most 16 (potentially empty) thin sets S1,,S16 that correspond to the sets S𝖫𝖫,S𝖫𝖱,S𝖱𝖫,S𝖱𝖱 for each of the levels V0,V1,V2,V3. We call such a partition of V(T)L a thin partition for (T,). Our colouring of elements of U consists of 32 colours A1,,A16,B1,,B16. Here Ai is the union of representative sets (Asi)sSi chosen to satisfy Lemma 10 for every i[16] and each Bi contains exactly one (arbitrary) element per representative set Asi, sSi. We call such a 32-colouring of U an identifying colouring for the thin partition (S1,,S16). An identifying colouring is simply an identifying colouring obtained from any partition (S1,,S16) of V(T)L into thin sets.

We now show how an identifying colouring can be used to identify in MSO one representative element of U for each inner node of the laminar tree of a laminar set system (U,). For this, we use representative sets and then the arbitrarily chosen element from each representative set as the leader which will play the role of the representative element. Before we proceed, let us first provide some auxiliary predicates.

For a set system (U,), we define 𝖱𝖮𝖮𝖳(X) and 𝖫𝖤𝖠𝖥(X) to be a predicates with one free set variable expressing that X corresponds to the root (or leaf, resp.) of the laminar tree of (U,) which is easy to implement in MSO. Furthermore, for a unary predicate A we additionally define a predicate with one free set variable 𝖫𝖤𝖠𝖥A(X) that requires X to correspond to a leaf of the laminar tree and the element contained in X additionally needs to satisfy predicate A. We further introduce predicates 𝖠𝖭𝖢(X,Y), 𝖣𝖤𝖲𝖢(X,Y), 𝖯𝖠𝖱𝖤𝖭𝖳(X,Y) and 𝖢𝖧𝖨𝖫𝖣(X,Y) each having two free set variables X,Y and which are satisfied if X,Y and the node corresponding to X in the laminar tree of (U,) is the ancestor, descendant, parent or child, respectively, of the node corresponding to Y. It is routine to implement these four predicates in MSO.

The following lemma implements predicates that provide the identification between sets in and elements in U in such a way that all elements in Ai, i{1,,16} receive distinct representatives which are exactly the leaders of their respective representative sets. We use one leader predicate 𝗅𝖾𝖺𝖽𝖾𝗋i for every i{1,,16} which provides the leaders for sets corresponding to nodes in Ai. We use an additional predicate 𝗅𝖾𝖺𝖽𝖾𝗋0 which gives the identification between singleton sets and elements. While this is trivial, it is convenient later to avoid exceptions for singleton sets.

Lemma 16.

Let (U,) be a laminar set system, (S1,,S16) a thin partition for (U,) and A1,,A16,B1,,B16 an identifying colouring for (S1,,S16). Using the unary predicates A1,,A16,B1,,B16 we can define binary predicates 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) for every i{0,,16} such that for every X there exists exactly one i{0,,16} and one rU, the leader of X, for which 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) is satisfied. Furthermore, for every i{0,,16} the elements rU for which there is a set X such that 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) is satisfied are pairwise different.

Proof.

Our goal is to define the conditions of Lemma 11 in MSO. For this, note that instead of using nodes of the laminar tree, which we do not have access to, in the definition of the sentence 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X), we rely on the fact that each node corresponds to a unique set in and use sets of instead of nodes. We MSO define the leader in this way for every inner node of the laminar tree. For the leaves which correspond to all singleton sets in we additionally define the trivial predicate 𝗅𝖾𝖺𝖽𝖾𝗋0(r,X):=𝖫𝖤𝖠𝖥(X)rX which assigns every singleton {r} the leader r.

To implement Lemma 11 in MSO we first require predicates that define when a set of is fully branched, single branched or missed by the set defined by any of the predicates Ai. Hence, fix any unary predicate A representing a subset of U. For any A{A1,,A16} we define three predicates with one free set variable X each expressing that X and the node corresponding to X in the laminar tree of (U,) is fully branched, single branched or missed by A as follows:

𝖿𝗎𝗅𝗅𝗒A(X):= 𝖲𝖤𝖳(X)¬𝖫𝖤𝖠𝖥(X)Y(𝖢𝖧𝖨𝖫𝖣(Y,X)y(yYA(y))),
𝗌𝗂𝗇𝗀𝗅𝖾A(X):= 𝖲𝖤𝖳(X)¬𝖫𝖤𝖠𝖥(X)Y(𝖢𝖧𝖨𝖫𝖣(Y,X)y(yYA(y))
Z[(𝖢𝖧𝖨𝖫𝖣(Z,X)z(zZA(z)))Z=Y]),
𝗆𝗂𝗌𝗌𝖾𝖽A(X):= 𝖲𝖤𝖳(X)¬𝖫𝖤𝖠𝖥(X)x(xX¬A(x)).

Since A1,,A16,B1,,B16 is an identifying colouring for (S1,,S16) each Ai is the union of representative sets (Asi)sSi with pairwise disjoint up-trees HAsi. Hence, by Lemma 11 any set RU and set X corresponding to inner node s of the laminar tree satisfy R=Asi if and only if they satisfy predicate 𝖱𝖤𝖯A(R,X) defined below. Note that we first define the following auxiliary predicate 𝖱𝖤𝖯A(R,X) which implements precisely conditions (i)-(iv) of Lemma 11 and then 𝖱𝖤𝖯A(R,X) extends 𝖱𝖤𝖯A(R,X) to also include property (v). This is needed because property (v) states that set R is maximal among all sets for which there is a node and they satisfy (i)-(iv). We define 𝖱𝖤𝖯A(R,X) as follows:
𝖱𝖤𝖯A(R ,X):=𝖲𝖤𝖳(X) x(xRA(x)) (i) Y(𝖢𝖧𝖨𝖫𝖣(Y,X)RYY[(𝖣𝖤𝖲𝖢(Y,X)RY)Y=Y]) (ii) Y((𝖣𝖤𝖲𝖢(Y,X)𝖿𝗎𝗅𝗅𝗒A(Y))Z[𝖢𝖧𝖨𝖫𝖣(Z,Y)(𝖫𝖤𝖠𝖥A(Z)𝗌𝗂𝗇𝗀𝗅𝖾A(Z))]) (iii) Y((𝖣𝖤𝖲𝖢(Y,X)𝗌𝗂𝗇𝗀𝗅𝖾A(Y))Z[𝖢𝖧𝖨𝖫𝖣(Z,Y)(𝖫𝖤𝖠𝖥A(Z)𝖿𝗎𝗅𝗅𝗒A(Z)) Z((𝖢𝖧𝖨𝖫𝖣(Z,Y)ZZ)𝗆𝗂𝗌𝗌𝖾𝖽A(Z))]). (iv)

Note that each line of the formula above expresses precisely one condition from Lemma 11 (excluding (v)) and we additionally have to ensure that X corresponds to a node and therefore is in . We define 𝖱𝖤𝖯A(R,X) as follow:

𝖱𝖤𝖯A(R,X):=𝖱𝖤𝖯A(R,X)XR((𝖠𝖭𝖢(X,X) 𝖱𝖤𝖯A(R,X))
(R=RX=X)). (v)

For a fixed i[16] Lemma 11 implies that for every X corresponding to node s of the laminar tree there is a unique set R, namely R=Asi, for which 𝖱𝖤𝖯Ai(R,X) is satisfied. Note that since for every i[16] the up-trees HAsi are pairwise disjoint for all sSi the representative sets (Asi)sSi must also be pairwise disjoint. Hence, by choosing an arbitrary element in Asi as the leader, as the unary predicates B1,,B16 do, we obtain that each set X obtains a unique element as leader (its precisely the element contained in Asi which satisfies predicates Bi). Hence, for i[16] we can define the predicate 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) as follows:

𝗅𝖾𝖺𝖽𝖾𝗋i(r,X):=Bi(r)R(rR𝖱𝖤𝖯Ai(R,X)).

Note that since for fixed i[16] the Asi are pairwise disjoint, it is also guarantied that for any fixed i[16] all leaders of nodes in Ai are pairwise different. This concludes the proof of the lemma.

3.3 The transduction

We have introduced all the tools to provide our transduction. We recall Theorem 2 here in a slighly different version. We note however that by a simple interpretation which forgets the 𝖲𝖤𝖳 predicate, we can obtain the original version.

Theorem 2.

There is an overlay MSO-transduction τ from {𝖲𝖤𝖳}-structure to {𝖽𝖾𝗌𝖼,𝖲𝖤𝖳}-structures such that for any laminar set system (U,) represented by the {SET}-structure 𝔽 the image of τ is the {𝖽𝖾𝗌𝖼,𝖲𝖤𝖳}-structure 𝔽𝕋 where 𝕋 is the {𝖽𝖾𝗌𝖼}-structure which represents the laminar tree T of (U,).

Proof of Theorem 2.

Let (U,) be a laminar set system and 𝔽 be the {𝖲𝖤𝖳}-structure representing (U,). Our MSO-transduction is obtained by composing the following atomic MSO-transductions. To define the transduction, we use the formula constructed in Lemma 16.

  1. 1.

    We first apply a 32-colouring transduction to structure 𝔽 and obtain unary predicates 𝖼𝗈𝗅𝗈𝗎𝗋1,,𝖼𝗈𝗅𝗈𝗎𝗋32.

  2. 2.

    We use filtering to ensure that the colouring 𝖼𝗈𝗅𝗈𝗎𝗋1,,𝖼𝗈𝗅𝗈𝗎𝗋32 is essentially identifying leaders correctly. To achieve this, we need to check that for every set X there is precisely one i{0,,16} and one rU for which the predicate 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) is satisfied. Furthermore, we need to ensure that for every i{0,,16} and any two distinct sets X,X for which there are elements r,r such that 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) and 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) are satisfied, it holds that rr. Note that we can express both these properties through an MSO sentence χ. By Lemma 15 and Lemma 10 there must always exist an identifying colouring while Lemma 16 guarantees that if 𝖼𝗈𝗅𝗈𝗎𝗋1,,𝖼𝗈𝗅𝗈𝗎𝗋32 is an identifying colouring then the properties we filter by must be satisfied. Hence, for any laminar set system 𝔽 we must receive at least one output.

  3. 3.

    Copy the resulting structure 16 times (resulting in 17 instances of each element of U), thus introducing 16 additional binary relations (𝖼𝗈𝗉𝗒i)i{0,,16} where 𝖼𝗈𝗉𝗒i(x,y) for i>0 indicates that x is the i-th copy of the original element y and 𝖼𝗈𝗉𝗒0(x,y) indicates that x is the original element y. Note that the original elements will represent the leaves of the laminar tree while the i-th copy of the leader of a set X will play the role of the node t corresponding to X in case the appropriate thin set Si contains t.

  4. 4.

    Finally, we interpret using a pair of formulas (ϕ,ψ𝖣𝖤𝖲𝖢) to obtain a {𝖣𝖤𝖲𝖢} structure 𝕋. For this we define the realization of a set X to be the i-th copy of the element rU for which 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) is satisfied. We define predicate 𝗋𝖾𝖺𝗅(x,X) expressing that x is the relization of X as follows:

    𝗋𝖾𝖺𝗅(x,X):=r(i{0,,16}(𝗅𝖾𝖺𝖽𝖾𝗋i(r,X)𝖼𝗈𝗉𝗒i(x,r))).

    Now ϕ restrict the universe to elements which are the realization of some set in . We define the descendant relationship of 𝕋 as follows. For two elements x,x of the restricted universe, ψ𝖣𝖤𝖲𝖢 defines that x is a descendant of x if they are the realization of two sets X,X and XX. Formally, let

    ψ𝖽𝖾𝗌𝖼(x,x):=XX(𝗋𝖾𝖺𝗅(x,X)𝗋𝖾𝖺𝗅(x,X)XX).

We are left to argue that the resulting structure 𝕋 represents the laminar tree T of (U,𝒮). For this we use the properties we ensured through filtering. First, we define a partition (P0,,P16) of as follows. Set X to be contained in part Pi if and only if there is rU for which 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) is satisfied. Note that this is a partition because for each X there is precisely one i{0,,16} and one rU for which this is the case (ensured by filtering). Because of filtering we know that for each i{0,,16} the leaders of sets in Pi are distinct. Our interpretation ensures that the i-th copy of the leader of set XPi is its realization and therefore plays the role of the node of T corresponding to X. This is ensured by the realization of XPi being the descendant of the realization of XPj exactly when XX. Finally, we note that for each set in there is precisely one element of 𝕋 which realizes it in the structure 𝕋 and hence 𝕋 represents the laminar tree T.

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Appendix A Beyond laminar set systems

In this section we shortly outline how to adjust the proofs of [4] to obtain Corollary 5, Corollary 6 and Corollary 7.

We first note that the transductions used to obtain [4, Theorem 1] uses the CMSO-transduction τ which given a laminar set system outputs its laminar tree [4, Theorem 2] as a black box. As stated in [4], the other steps of the transductions in [4, Theorem 1] are MSO-transductions. Roughly these transductions are of the form:

  1. 1.

    An MSO-interpretation that adds a predicate 𝖲𝖤𝖳 to recognize sets corresponding to a side of a separations (for bipartitions we take the side not containing a fixed element).

  2. 2.

    An MSO-interpretation that adds a predicate 𝖲𝖤𝖳! that recognizes the sets that do not cross any other.

  3. 3.

    As these sets are laminar, we can apply τ to transduce tree-structure.

  4. 4.

    An MSO-interpretation that adds the information to the tree-structure that is necessary to recover the original object.

Thus, by using our new MSO-transduction (Theorem 2) within the transductions given in [4] directly yields Corollary 5, Corollary 6 and Corollary 7 with the exception of the transduction outputting the bi-join decomposition of a graph. Although this transduction does not rely on counting aside from obtaining the laminar tree, it uses the representative predicate 𝗋𝖾𝗉A(x,X) which is defined within the transduction producing the laminar tree in a different way to the predicate given in this paper. The predicate is required for the following reason.

A bi-join of a graph G is a bipartition {X,Y} of G for which there exists subsets XX and YY such that X is complete to Y, XX is complete to YY and there are no further edges between X and Y. In the bi-join decomposition, an auxiliary vertex is introduced for each of the sets X,XX,Y and YY. However, structurally the bi-join is completely symmetric and therefore the representative of the node representing a particular bi-join in the decomposition is used to identify which of the sets X,XX,Y,YY is associated with which of the auxiliary vertices. To conclude, we can easily use our predicate 𝗅𝖾𝖺𝖽𝖾𝗋i(r,X) instead for this purpose.