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14 results · all verified · 0 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 14 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Induced Subgraphs and Hereditary Graph Classes

1 · Prerequisites

2 · Summary

Published finite-simple-graph, induced-subgraph, graph-isomorphism, complementation, connectivity, and finite-counting definitions provide the setting. An induced embedding preserves both adjacency and nonadjacency, so its image is stricter than an ordinary subgraph copy; the induced-copy number counts such injective maps rather than unlabelled vertex subsets.

Composition of induced embeddings makes H-freeness hereditary, and every hereditary class is characterized by its possibly infinite minimal forbidden induced subgraphs. Complementation transports classes, bases, cliques and stable sets. Connectedness and anticonnectedness then organize component decompositions, while complete, anticomplete, pure and mixed disjoint vertex-set pairs record their cross-edge patterns.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-01Open item page →

Induced embeddings and induced copies of a graph

Definition

Let H and G be finite simple graphs (A finite simple graph is a finite vertex set together with a set of two-element vertex subsets). An induced embedding of H in G is an injection φ:V(H)V(G) such that, for all distinct x,yV(H),

xyE(H)φ(x)φ(y)E(G).

Thus φ preserves both adjacency and nonadjacency (Injection, surjection, bijection). Its image G[φ(V(H))] is an induced copy of H in G: the restricted map is an isomorphism from H onto that induced subgraph (Subgraphs, induced subgraphs and spanning subgraphs, Graph isomorphisms, automorphisms and graph complements).

We say that H is an induced subgraph of G up to isomorphism when such an embedding exists.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Induced embeddings compose, and the induced-subgraph relation is transitive up to isomorphism

Statement

If φ:HindG and ψ:GindK, then ψφ:HindK. Consequently, being an induced subgraph up to isomorphism is transitive.

Facts & Assumptions

Given: Induced embeddings φ:HG and ψ:GK.

[F1]

An induced embedding is injective and preserves adjacency in both directions (Induced embeddings and induced copies of a graph).

[F2]

A composite of injections is injective (Injection, surjection, bijection).

[F3]

Graph isomorphism is compatible with composition (Graph isomorphisms, automorphisms and graph complements).

Proof

technique · direct
1.1

The composite ψφ is injective.

F2
1.2

For distinct x,yV(H), one has xyE(H) if and only if φ(x)φ(y)E(G), if and only if ψφ(x)ψφ(y)E(K).

F1
2.1

Hence ψφ is an induced embedding. Replacing induced copies by their isomorphic representatives gives the stated transitivity up to isomorphism.

step 1.1step 1.2F1F3
DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-01Open item page →

The induced-embedding count indH(G)

Definition

For finite graphs H and G, define the induced-embedding count

indH(G):={φ:V(H)V(G):φ is an induced embedding}.

The set inside the cardinality is a subset of the finite function set V(G)V(H), so the displayed natural number is well defined (The set AB of functions BA between finite sets is finite, with AB=AB, A subset of a finite set is finite, with BA, and equality holds if and only if B=A, The cardinality A of a finite set).

This convention counts labelled embeddings, not vertex subsets. An induced copy with image W contributes one embedding for each isomorphism HG[W] (Induced embeddings and induced copies of a graph).

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

indH(G) is isomorphism-invariant and equals indH(G)

Statement

If HH and GG, then

indH(G)=indH(G).

Moreover,

indH(G)=indH(G).

Facts & Assumptions

Given: Finite graphs H,H,G,G with isomorphisms a:HH and b:GG.

[F1]

indH(G) is the finite cardinality of the induced-embedding set (The induced-embedding count indH(G)).

[F2]

Isomorphisms and induced embeddings preserve adjacency and nonadjacency in both directions (Induced embeddings and induced copies of a graph, Graph isomorphisms, automorphisms and graph complements).

Proof

technique · direct bijections
1.1

The assignment φbφa sends induced embeddings HG to induced embeddings HG.

F2
1.2

The same vertex map φ is an induced embedding HG exactly when it is an induced embedding HG, because complementation reverses both adjacency tests simultaneously.

F2
2.1

Its inverse is θb1θa1, so it is a bijection and the first equality follows.

step 1.1F1F2
3.1

The identity on maps is therefore a bijection between these embedding sets, proving the complement equality.

step 1.2F1
DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-08-01Open item page →

H-free and F-free graphs under the induced-subgraph convention

Definition

For finite graphs H and G, the graph G is H-free when G has no induced copy of H (Induced embeddings and induced copies of a graph). Equivalently,

indH(G)=0

(The induced-embedding count indH(G)).

For a family F of finite graphs, a finite graph G is F-free when it is H-free for every HF. Throughout this page, “free” always refers to induced subgraphs. It does not merely prohibit ordinary subgraph copies.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Every induced subgraph of an F-free graph is F-free

Statement

If G is F-free and J is an induced subgraph of G, then J is F-free.

Facts & Assumptions

Given: An F-free graph G and an induced embedding ι:JG.

[F1]

F-free means that no HF has an induced embedding into G (H-free and F-free graphs under the induced-subgraph convention).

Proof

technique · contradiction
1.1

Suppose J is not F-free. Then some HF has an induced embedding φ:HJ.

assume-contraF1
2.1

The composite ιφ:HG is an induced embedding.

step 1.1L1
3.1

This contradicts that G is F-free. Hence J is F-free.

step 2.1F1discharge-contradiction
DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-08-01Open item page →

Hereditary graph classes

Definition

A class C of finite simple graphs is a hereditary graph class when:

  1. it is closed under isomorphism; and
  2. whenever GC and H has an induced embedding into G, one has HC.

The second clause is closure under taking induced subgraphs (Induced embeddings and induced copies of a graph). Isomorphism closure makes membership depend only on graph structure, not on the chosen vertex labels (Graph isomorphisms, automorphisms and graph complements).

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Every class defined by forbidden induced subgraphs is hereditary

Statement

For every family F of finite graphs, the class of all F-free finite graphs is hereditary.

Facts & Assumptions

Given: A family F of finite graphs.

[L1]

Induced subgraphs of an F-free graph remain F-free (Every induced subgraph of an F-free graph is F-free).

[F1]

F-freeness is invariant under graph isomorphism (H-free and F-free graphs under the induced-subgraph convention).

[F2]

Heredity means closure under isomorphism and induced subgraphs (Hereditary graph classes).

Proof

technique · direct
1.1

The class of F-free graphs is closed under isomorphism because an isomorphism transports every induced copy in both directions.

F1
1.2

It is closed under induced subgraphs by L1.

L1
2.1

These are exactly the two requirements for a hereditary class.

step 1.1step 1.2F2
DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-08-01Open item page →

Minimal forbidden induced subgraphs and forbidden bases

Definition

Let C be a hereditary class. A finite graph H is a minimal forbidden induced subgraph for C when

HC,

but every proper induced subgraph H[W] with WV(H) belongs to C (Subgraphs, induced subgraphs and spanning subgraphs).

The minimal forbidden basis B(C) is the class of all such graphs, understood up to isomorphism. More generally, a family F is a forbidden induced-subgraph basis for C when GC exactly when G is F-free (H-free and F-free graphs under the induced-subgraph convention). Minimality here concerns proper induced subgraphs, not ordinary subgraphs (Induced embeddings and induced copies of a graph, Hereditary graph classes).

TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Every hereditary graph class is determined by its unique minimal forbidden induced subgraphs

Statement

For every hereditary graph class C and every finite graph G,

GCG is B(C)-free.

If F is any forbidden induced-subgraph basis for C, then for every HB(C), the family F contains a graph isomorphic to H. Consequently, B(C) is, up to isomorphism, the unique inclusion-minimal forbidden basis for C.

Facts & Assumptions

Given: A hereditary class C and a finite graph G.

[F1]

Membership in C passes to induced subgraphs and is invariant under graph isomorphism (Hereditary graph classes).

[F2]

B(C) consists exactly of graphs outside C all of whose proper induced subgraphs lie in C (Minimal forbidden induced subgraphs and forbidden bases).

[F3]

A finite vertex set has finitely many subsets, whose cardinalities are natural numbers; every nonempty set of natural numbers has a least element (The cardinality A of a finite set, P(A)=2A for finite A, The well-ordering principle).

[F4]

F-free means containing no induced member of F (H-free and F-free graphs under the induced-subgraph convention).

Proof

technique · direct
1.1

If GC, then no induced subgraph of G lies outside C, so in particular G contains no member of B(C).

F1F2
1.2

Suppose GC. Among vertex sets WV(G) for which G[W]C, choose one of least cardinality; it exists because W=V(G) is available.

chooseF3
1.3

Let F be any forbidden induced-subgraph basis for C. Every JF lies outside C: otherwise JC would contain itself as an induced copy of a member of F, contradicting the defining equivalence for F.

F4
2.1

Every proper induced subgraph of G[W] lies in C by minimality of W. Hence G[W]B(C).

step 1.2F2
2.2

Fix HB(C). Since HC, it is not F-free, so some JF occurs as an induced subgraph of H. If that copy were proper, then it would lie in C by the minimality of H; closure under isomorphism would give JC, contradicting step 1.3. Thus the copy uses all vertices of H, and JH.

F1F2F4step 1.3
3.1

Thus G is not B(C)-free. Together with step 1.1 this proves the equivalence.

step 2.1step 1.1F4
4.1

Hence every forbidden basis for C contains, up to isomorphism, every member of B(C). Since B(C) is itself a basis by step 3.1, it is inclusion-minimal, and any inclusion-minimal forbidden basis has no additional members. This proves uniqueness up to isomorphism.

step 3.1step 2.2
CorollaryStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Every nonempty hereditary graph class contains the null graph

Statement

Every nonempty hereditary class of finite graphs contains the null graph K0.

Facts & Assumptions

Given: A nonempty hereditary graph class C.

[F1]

Choose GC.

[F3]

A hereditary class contains every induced subgraph of each member (Hereditary graph classes).

Proof

technique · direct
1.1

Since GC, heredity gives G[]C.

F1F3
2.1

Since G[]=K0, the null graph belongs to C.

step 1.1F2
DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-01Open item page →

The complement of a graph class

Definition

For a graph class C, its complement class is

C:={G:GC}.

It consists exactly of those graphs whose complements belong to C. If C is isomorphism-closed, this is equivalently the isomorphism-closed class of complements of members of C (Graph isomorphisms, automorphisms and graph complements). The notation does not mean set-theoretic complement inside the class of all graphs.

When C is isomorphism-closed, so is C, because an isomorphism of graphs induces an isomorphism of their complements.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

G[W]=G[W] for every vertex set W

Statement

For every finite graph G and every WV(G),

G[W]=G[W]

as graphs on vertex set W.

Facts & Assumptions

Given: A graph G and WV(G).

[F1]

G[W] retains exactly the edges of G with both endpoints in W (Subgraphs, induced subgraphs and spanning subgraphs).

[F2]

Complementation replaces adjacency by nonadjacency between distinct vertices (Graph isomorphisms, automorphisms and graph complements).

Proof

technique · direct
1.1

Both displayed graphs have vertex set W.

F1F2
1.2

For distinct x,yW, xy is an edge of G[W] if and only if it is not an edge of G[W], if and only if it is not an edge of G, if and only if it is an edge of G[W].

F1F2
2.1

Their vertex and edge sets are equal, so the graphs are equal.

step 1.1step 1.2
TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Complementation preserves hereditary classes and complements their minimal forbidden bases

Statement

If C is hereditary, then C is hereditary and

B(C)={H:HB(C)}

up to isomorphism.

Facts & Assumptions

Given: A hereditary graph class C.

[F1]

GC exactly when GC (The complement of a graph class).

[L1]

Complementation commutes with taking induced subgraphs (G[W]=G[W] for every vertex set W).

[F2]

A minimal forbidden graph lies outside the class while all its proper induced subgraphs lie inside (Minimal forbidden induced subgraphs and forbidden bases).

[L2]

A hereditary class is determined by its unique minimal forbidden basis (Every hereditary graph class is determined by its unique minimal forbidden induced subgraphs).

Proof

technique · direct
1.1

Let GC and WV(G). Then GC, so G[W]C by heredity.

F1
1.2

Let HB(C). Then HC, while for every proper WV(H), H[W]C and therefore H[W]=H[W]C.

F1F2L1
2.1

Since G[W]=G[W], one has G[W]C. Isomorphism closure is likewise preserved, so C is hereditary.

step 1.1L1F1
2.2

Hence HB(C). Applying the same argument to the involution of complementation gives the reverse inclusion.

step 1.2F2
3.1

Therefore the minimal bases are complementary as claimed.

step 2.2L2
CorollaryStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

G is H-free if and only if G is H-free

Statement

For finite graphs G and H,

G is H-freeG is H-free.

Facts & Assumptions

Given: Finite graphs G and H.

[F1]

H-free means containing no induced copy of H (H-free and F-free graphs under the induced-subgraph convention).

[L1]

Complementation commutes with induced subgraphs (G[W]=G[W] for every vertex set W).

[F2]

Complementation carries isomorphisms HG[W] to isomorphisms HG[W] (Graph isomorphisms, automorphisms and graph complements).

Proof

technique · direct
1.1

For every WV(G), one has G[W]H if and only if G[W]=G[W]H.

L1F2
2.1

Thus G contains an induced H if and only if G contains an induced H. Negating both sides gives the claimed equivalence.

step 1.1F1
DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-01Open item page →

Cliques, stable sets, the clique number ω(G) and stability number α(G)

Definition

Let G be a finite simple graph. A set XV(G) is a clique when every two distinct vertices of X are adjacent, equivalently when G[X] is complete. It is a stable set, or independent set, when no two distinct vertices of X are adjacent, equivalently when G[X] is edgeless (Subgraphs, induced subgraphs and spanning subgraphs, Empty and complete graphs, complete bipartite graphs, and the convention that Pn and Cn have n vertices).

The clique number and stability number are

ω(G):=max{X:X is a clique in G},α(G):=max{X:X is a stable set in G}.

Both maxima exist because the families are nonempty, containing , and lie inside the finite power set of V(G) (The cardinality A of a finite set, P(A)=2A for finite A, A subset of a finite set is finite, with BA, and equality holds if and only if B=A, The well-ordering principle). In particular, ω(K0)=α(K0)=0.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Complementation swaps cliques with stable sets, so ω(G)=α(G)

Statement

For every finite graph G, a vertex set is a clique in G if and only if it is a stable set in G. Consequently,

ω(G)=α(G),α(G)=ω(G).

Facts & Assumptions

Given: A finite graph G and XV(G).

[F1]

A clique has all possible edges among its vertices, while a stable set has none (Cliques, stable sets, the clique number ω(G) and stability number α(G)).

[F2]

Distinct vertices are adjacent in G exactly when they are nonadjacent in G (Graph isomorphisms, automorphisms and graph complements).

Proof

technique · direct
1.1

Every pair of distinct vertices in X is adjacent in G if and only if no such pair is adjacent in G.

F2
2.1

Thus X is a clique in G if and only if it is stable in G, and symmetrically X is stable in G if and only if it is a clique in G.

step 1.1F1
3.1

The same vertex sets occur in the paired maximizations and retain their cardinalities, so the two displayed equalities follow.

step 2.1F1
DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-01Open item page →

Anticonnected graphs and anticonnected components

Definition

A graph G is anticonnected, or co-connected, when its complement G is connected (Connected graphs and connected components defined by the existence of vertex paths, Graph isomorphisms, automorphisms and graph complements).

An anticonnected component, or anticomponent, of G is a vertex set AV(G) that is the vertex set of a connected component of G. Equivalently, G[A] is anticonnected and A is inclusion-maximal with that property (Subgraphs, induced subgraphs and spanning subgraphs).

Under the library convention, the null graph is not anticonnected, while a one-vertex graph is anticonnected.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

The anticonnected components of G are exactly the connected components of G

Statement

For every graph G, its anticomponents are exactly the vertex sets of the connected components of G. In particular, they partition V(G).

Facts & Assumptions

Given: A finite graph G.

[F1]

Anticomponents are defined to be the component vertex sets of G (Anticonnected graphs and anticonnected components).

[L2]

Proof

technique · direct
1.1

By F1, a set A is an anticomponent of G exactly when it is the vertex set of a connected component of G.

F1
2.1

Equivalently, G[A]=G[A] is connected and A is maximal with this property.

step 1.1L2
3.1

The component partition theorem applied to G shows that these sets partition V(G)=V(G).

step 1.1L1
TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Every graph with at least two vertices is connected or anticonnected

Statement

Every finite graph with at least two vertices is connected or anticonnected. Equivalently, if G is disconnected and nontrivial, then G is connected.

Facts & Assumptions

Given: A finite graph G with V(G)2.

[F1]

G is anticonnected exactly when G is connected (Anticonnected graphs and anticonnected components).

[F2]

Vertices in different components of G are nonadjacent in G and hence adjacent in G (Graph isomorphisms, automorphisms and graph complements).

Proof

technique · direct
1.1

If G is connected, the first alternative holds. Suppose instead that G is disconnected.

Given
1.2

Let x,yV(G). If x=y, the length-zero path joins them. If they are distinct and lie in different components, then xyE(G).

L1F2
1.3

If xy and they lie in the same component, choose a vertex z in a different component. Then xz,zyE(G), so x,z,y is an x-y path in G.

L1F2
2.1

Every two vertices are therefore joined in G, so G is connected and G is anticonnected.

step 1.2step 1.3F1
DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-01Open item page →

Edges between disjoint vertex sets; complete, anticomplete, pure and mixed pairs

Definition

Let G be a finite simple graph and let A,BV(G) be disjoint. An edge between A and B is an edge ab with aA and bB.

The pair (A,B) is:

  • complete when every aA is adjacent to every bB;
  • anticomplete when no aA is adjacent to any bB;
  • pure when it is complete or anticomplete; and
  • mixed when it is neither complete nor anticomplete.

Adjacency is the symmetric edge relation of G (A finite simple graph is a finite vertex set together with a set of two-element vertex subsets, Adjacency, incidence, open and closed neighbourhoods, vertex degree, minimum degree and maximum degree). If A= or B=, the pair is both complete and anticomplete, hence pure and not mixed.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Purity is symmetric; complementation swaps complete and anticomplete pairs and preserves mixed pairs

Statement

For disjoint vertex sets A,B in a graph G:

  1. (A,B) is complete, anticomplete, pure or mixed exactly when (B,A) has the same property;
  2. complementation swaps complete pairs with anticomplete pairs; and
  3. complementation preserves pure pairs and mixed pairs.

Facts & Assumptions

Given: A graph G and disjoint sets A,BV(G).

[F1]

Complete, anticomplete, pure and mixed pairs are defined by the cross-pair adjacency pattern (Edges between disjoint vertex sets; complete, anticomplete, pure and mixed pairs).

[F2]

Graph adjacency is symmetric, and complementation exchanges adjacency with nonadjacency between distinct vertices (Graph isomorphisms, automorphisms and graph complements).

Proof

technique · direct
1.1

Since ab and ba describe the same edge, reversing the ordered pair of sets changes none of the four properties.

F1F2
1.2

Every cross pair is an edge of G exactly when no cross pair is an edge of G; likewise, no cross pair is an edge of G exactly when every cross pair is an edge of G.

F2
2.1

Hence complementation swaps complete and anticomplete pairs. It therefore preserves their union, the pure pairs, and its complement, the mixed pairs.

step 1.2F1
3.1

Together with symmetry from step 1.1, this proves all assertions.

step 1.1step 2.1
LemmaStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-01Open item page →

Distinct connected components are anticomplete, and distinct anticonnected components are complete

Statement

Distinct connected components of a graph are anticomplete to one another. Distinct anticomponents are complete to one another.

Facts & Assumptions

Given: A finite graph G.

[L1]

Connected components partition the vertices into maximal connected parts (The connected components of a graph partition its vertex set and are its maximal connected subgraphs).

[L2]

Anticomponents of G are connected components of G (The anticonnected components of G are exactly the connected components of G).

[L3]
[F1]

Anticomplete and complete pairs have respectively no and all cross edges (Edges between disjoint vertex sets; complete, anticomplete, pure and mixed pairs).

Proof

technique · direct
1.1

No edge joins two distinct connected components, since such an edge would connect them into one component. Thus distinct components are anticomplete.

L1F1
2.1

Distinct anticomponents of G are distinct connected components of G, so they are anticomplete in G by step 1.1 applied there.

L2step 1.1
3.1

Complementing back makes those two sets complete in G.

step 2.1L3

5 · Examples, counterexamples and false statements

None yet.

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