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A planar graph admits at least one graph drawing in which edges meet only at common endpoints. This is a property of the abstract graph, not a requirement on every graph drawing. A plane graph specifies one such drawing.^[ref]

For example, the complete graph $K_4$ joins every pair of four vertices, giving six edges. Put its vertices at the corners of a square and its diagonals cross. Instead put $A,B,C$ at the corners of a triangle and $D$ inside it: the same six edges $AB,AC,BC,AD,BD,CD$ now meet only at their endpoints. The first graph drawing obscures planarity; the second proves it. In particular, its crossing number is zero.

The distinction matters whenever connections must fit on one surface without intersecting. A failed graph drawing may only need rearrangement, whereas nonplanarity by edge counts proves that every rearrangement fails.

A plane graph is a planar graph with a specified graph drawing whose edges meet only at common endpoints, also called an embedding in the Euclidean plane. Its faces of a plane graph and facial boundary walks refer to that embedding. This geometric use of embedding differs from a graph embedding, which maps one abstract graph into another.

Choose a uniform $m$-subset $Z$ and the graph consisting only of its clique. Acceptance by two approximating set families but rejection by their intersection requires two minimal witnesses whose union has size greater than $\ell$. At least one has size greater than $\ell /2$. The sunflower bound for minimal members of a Razborov-closed family and $\mathbb{P}(W\subseteq Z)\le (m/n{)}^{|W|}$ give $2{\sum }_{s>\ell /2}s!(r-1{)}^s(m/n{)}^s\le 4q^{\ell /2}$.

The ferromagnetic Potts model on a finite graph assigns $q$ possible states to each graph vertex and rewards neighbouring equal states by the displayed weight, for $\beta \ge 0$. The interaction sum counts each unoriented edge once. The Edwards-Sokal coupling identifies its spin clusters with the random-cluster model at $p=1-e^{-\beta }$ and integer $q\ge 2$.

The power set $\mathcal{P}(X)$ is the set of all subsets of $X$, including the empty set and $X$ itself. It is useful whenever an object is a selection rather than an individual member: a possible collection of active vertices in a graph, for example, belongs to the power set of the vertex set.

For $X=\{a,b,c\}$, the power set has eight members:Each of the three set elements is either included or excluded. More generally, if $|X|=n<\infty$, these $n$ independent binary choices give $|\mathcal{P}(X)|=2^n$. This is a bijection between subsets and binary strings of length $n$, not a probabilistic independence assumption.

Ordering the subsets by inclusion gives a Boolean lattice. In the picture an upward edge adds one member. Complementation reverses inclusion, and union and intersection combine selections. The empty-input case is worth checking: $\mathcal{P}(\varnothing )=\{\varnothing \}$ has one member, not zero, consistently with $2^0=1$.

The graph with one vertex per variable and an edge between distinct variables occurring in a common constraint scope. Each hypergraph edge of the constraint hypergraph becomes a clique. A constraint satisfaction problem consisting only of all-different constraints on a common constraint domain is exactly a proper graph colouring problem on this primal graph.

A transitive subgroup $G\le S_p$ for prime $p$ that contains a transposition is $S_p$. Form a graph on the $p$ letters with edges given by all conjugates in $G$ of that transposition. The transitive action permutes connected components, so their common size divides $p$. An edge rules out size one, hence the graph is connected. Its edge transpositions generate $S_p$: successive swaps along a path generate the swap of its endpoint with any chosen start. All these edge transpositions belong to $G$.

For prime $p$, a graph with $m>(p-1)n$ edges and maximum degree at most $2p-1$ contains a nonempty $p$-regular subgraph. Over ${\mathbb{F}}_p$, apply the Combinatorial Nullstellensatz on the Boolean edge-variable cube toIts full squarefree coefficient is nonzero because the first product has degree below $m$. A nonzero value cannot occur at the zero vector; at any other Boolean vector the second product vanishes, and the first forces all selected degrees to be multiples of $p$. The maximum-degree condition makes every positive selected degree exactly $p$.

Start from one graph vertex. Repeatedly add the least-weight edge joining the reached graph vertices to an unreached graph vertex. Each step grows a tree and obeys the minimum spanning tree cut property. On a finite connected graph, the result is a minimum spanning tree.

A proper vertex coloring assigns labels to the vertices of a graph so that vertices joined by an edge have different labels. Each colour class is an independent set. The chromatic number is the least number of labels needed for this form of graph coloring.

For $n\ge 2$, a Prüfer sequence is the encoding of a labelled tree on $\{1,\dots ,n\}$ by a sequenceRepeated labels are allowed, and their order matters. To encode the tree, repeatedly remove its smallest-labelled leaf of a graph together with its incident edge, recording the label of its unique neighbour. Stop when two vertices remain; do not record their final edge. Removing a leaf of a graph leaves a tree, so the procedure makes exactly $n-2$ deletions. The choice of the smallest label makes the encoding deterministic. ^[ref]

For a concrete example, take six vertices and the set of edgesInitially $2,4,6$ label the vertices of vertex degree one. Remove $2$ and record its neighbour $1$. Now $1$ has become a leaf of a graph, so remove $1$ and record $3$. Next remove $4$ and record $3$ again; then remove $3$ and record $5$. The final edge joins $5$ and $6$. Thus the Prüfer sequence isThe deletion order is $2,1,4,3$: removing a leaf of a graph can make its neighbour a leaf of a graph, so the eligible labels change as the tree shrinks.

To decode, keep a set $L$ of remaining labels, initially $\{1,\dots ,n\}$, and the remaining sequence, initially $p$. While the sequence is nonempty, let $v$ be the smallest label in $L$ absent from the entire remaining sequence. Add an edge between $v$ and its first entry, then remove $v$ from $L$ and delete that first entry. Finally join the two labels left in $L$. For the example:

The decoder always has a choice: if the remaining sequence has length $m$, then $L$ has $m+2$ labels, so at least two of them are absent. Its first entry is still in $L$, and differs from the chosen absent label. Every removed vertex attaches to a vertex that survives longer; following these attachments eventually reaches the final pair. Consequently the decoded graph is connected, and its $n-1$ edges make it a tree. In this construction, each occurrence of $v$ contributes an incident edge, and $v$ gets one more edge at its final attachment. Thus a remaining label is absent from the remaining sequence exactly when it labels a leaf of a graph. Encoding and decoding choose the same smallest label at each step and undo each other. This bijection proves Cayley's formula and explains why every freely chosen sequence is valid. ^[ref]

For $n=2$ there is exactly one empty sequence, and decoding adds the sole edge. For $n=1$ there is one tree, with vertex degree zero, but no length-$n-2$ Prüfer sequence. This is the exception both to the encoding convention and to the Prüfer degree formula.

Heinz Prüfer's 1918 paper "Neuer Beweis eines Satzes über Permutationen" appeared in Archiv der Mathematik und Physik, third series, volume 27, pages 142–144. It gives an encoding proof of the labelled tree count. The historical title means "New proof of a theorem on permutations"; the modern Prüfer sequence permits repeated labels, so it is not a permutation. ^[ref]

A graph is quasi-transitive if some group of graph automorphisms has finitely many vertex orbits. This is also called a graph of finite type. A connected graph that is infinite and a locally finite graph then has uniformly bounded degrees. Every orbit is infinite: a finite invariant orbit would have finite neighborhoods, and finitely many orbit types would put the entire graph in one such neighborhood. This permits finite sets to be moved arbitrarily far apart by graph automorphisms.

The Rado graph is the unique countably infinite graph with the two-uniform hypergraph extension property. Equivalently, for any disjoint finite vertex sets $U,W$, some new vertex is adjacent to every member of $U$ and no member of $W$. The back-and-forth method proves uniqueness. Independent edge sampling on the natural numbers with any fixed $p\in (0,1)$ produces this graph almost surely. It contains both edges and nonedges and therefore is not a complete graph. Its first-order axioms constitute the theory of the random graph.

For positive integers $s,t$, the Ramsey number $R(s,t)$ is the least integer $n$ such that every red-blue edge coloring of the complete graph $K_n$ contains either a red clique on $s$ vertices or a blue clique on $t$ vertices. The complete graph $K_n$ has all $\left(\genfrac{}{}{0ex}{}{n}{2}\right)$ unordered pairs as edges; every edge gets exactly one color, with no requirement that incident edges have different colors. The definition and its graph interpretation appear in D. Yogeshwaran's Discrete Mathematics notes, section 5.3.

For example, $R(3,3)$ asks how many vertices force a monochromatic triangle in a graph. Swapping colors gives $R(s,t)=R(t,s)$. Also $R(2,t)=t$: either a red edge exists, or all edges are blue. On $t-1$ vertices, an all-blue coloring avoids both a red edge and a blue $t$-clique. Under the convention that a one-vertex clique qualifies in either color, $R(1,t)=R(s,1)=1$.

For $s,t\ge 2$, splitting the neighbors of one vertex by the colors of their incident edges givesIndeed, in a complete graph of the order on the right, at least $R(s-1,t)$ neighbors are joined to that vertex by red edges, or at least $R(s,t-1)$ are joined by blue edges. In the red case, the defining property of the smaller Ramsey number supplies either a blue $t$-clique among the neighbors or a red $(s-1)$-clique that extends by the chosen vertex; the blue case is symmetric. Starting from the one-vertex base cases, this recursively proves finite existence. The binomial upper bound for a Ramsey number packages the resulting estimate. ^[ref] present the neighbor argument and the sharp Ramsey number for triangles.

Equivalently, every graph on at least $R(s,t)$ vertices has an $s$-clique or a $t$-element independent set: color its edges red and the edges of its complement graph blue. This is the Ramsey theorem in graph theory. Larger monochromatic patterns have their own thresholds, such as a graph Ramsey number, matching Ramsey number or triangle-packing Ramsey number; these ask different questions from finding one clique.

Ramsey theory studies configurations that every finite coloring of a sufficiently large structure must contain. Its striking message is that some order is unavoidable even when no organized construction was used. A basic example is the Ramsey number for triangles: color every edge of a complete graph on six vertices red or blue, in any way, and some triangle in a graph has all three edges the same color. Five vertices do not suffice.

The objects receiving colors matter. In the Ramsey number for triangles, colors belong to edges, which represent all unordered pairs of distinct vertices; the vertices themselves have no colors. In the finite Ramsey theorem, colors can instead belong to all $r$-element subsets of a finite set. A monochromatic set or homogeneous set for a colouring expresses agreement among the appropriate colored objects.

Why is this useful? Ramsey theory provides existence guarantees in combinatorics that survive every possible assignment of a fixed finite number of colors. The Ramsey theorem turns this into a structural dichotomy: every sufficiently large graph contains a large clique or a large independent set. In arithmetic, the Van der Waerden theorem guarantees monochromatic arithmetic progressions. The beauty is the universal quantifier: an adversary can choose every color and still cannot prevent the promised configuration.

These guarantees concern precisely specified configurations under stated hypotheses. They do not justify selecting arbitrary patterns in empirical data after seeing the data, or attributing statistical significance to a selected pattern. A mathematical guarantee of some monochromatic configuration alone says nothing about the empirical meaning of that configuration.

The subject's name comes from F. P. Ramsey's ^[ref]. The ^[ref] records receipt on 28 November 1928 and presentation on 13 December 1928. Ramsey introduced the Ramsey theorem for r-sets and finite Ramsey theorem as tools for a problem in mathematical logic, while recognizing their independent interest in combinatorics. He also distinguished proving that a finite threshold exists from finding its smallest value, the quantitative question captured by a Ramsey number.

In a dense graph with many common neighbours for every pair of vertices, choose disjoint random small sets. A set is good if few vertices have no neighbour in it. The Markov inequality controls the number of bad sets, and random sampling makes almost every pair of good sets adjacent. Add unused common neighbours to connect each set and to repair the remaining missing adjacencies. If the number of used vertices stays below every common-neighbour count, this constructs disjoint branch sets of a graph minor representing a large complete graph.

Each vertex independently chooses $k$ distinct other vertices uniformly, and an undirected edge exists when either endpoint chooses the other. This has dependent undirected edges and is not a binomial random graph. Coupling the choices as the first $k$ entries of independent random permutations makes the graphs increasing in $k$.

A turn-based game on a directed graph in which a player tries to force a designated target set. Its winning region is a least fixed point: add that player's vertices with some winning successor and the opponent's vertices with all successors winning. The finite-graph version uses the same construction as a winning-position attractor in an infinite game.

With disjoint terminal wins for two players and infinite plays valued as draws, compute both reachability attractors while keeping terminals fixed. Entry ranks give terminating winning strategies; positions outside both regions admit defence avoiding a forced loss. A cycle alone is not a certificate of a draw if it also offers a forceable winning exit.

A vertex $v$ is reachable from $u$ if a finite path leads from $u$ to $v$, respecting edge directions in a directed graph. For a finite graph, graph search decides reachability by maintaining the set of discovered vertices; every step discovers a new vertex or examines an edge, so the procedure terminates.

A formal combination of chemical species appearing on one side of a chemical reaction. The coefficients $y_i$ are nonnegative integers. A chemical reaction network can be viewed as a directed graph whose vertices are reaction complexes and whose edges are chemical reactions. The empty complex represents external production or removal.