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A graph describes objects, called vertices, and pairwise connections, called edges. Its vertex set $V$ tells us what the objects are; $E$ records their connections. The basic abstraction retains graph adjacency while discarding positions, angles and Euclidean distances: moving the dots in a drawing changes none of the edges. A crossing of two drawn lines is not an extra vertex unless explicitly designated as one. Even the labels can change: a graph isomorphism is a relabelling that preserves graph adjacency in both directions.^[ref]

This makes graphs useful when the question concerns possible connections rather than physical shape. For example, model locations by vertices and direct links by edges; a graph path answers whether one location can reach another. Alternatively, let vertices be activities and edges represent conflicts: a graph coloring then assigns compatible time slots. These are mathematical models, so one must decide which pairwise relationship an edge means before applying graph theory.^[ref]

Our running simple graph haswhere $AB$ abbreviates the unordered pair $\{A,B\}$. It consists of two triangles in a graph, $ABC$ and $DEF$, joined by $CD$. The two drawings above have identical graph adjacency, even though their apparent lengths differ. What counts is that $C$ and $D$ are adjacent, whereas $A$ and $F$ are not.

Route questions require precise vocabulary. A walk is a sequence of consecutive adjacent vertices, permitting repetitions; its length counts traversed edges. A trail is a walk that repeats no edge; a graph path repeats no vertex. For a multigraph, the walk must also specify which parallel edge each step uses. For instance, $A,B,A,C,D,F$ is a length-five walk but not a trail, since it uses $AB$ twice. The length-three graph path $A,C,D,F$ is a shortest route: crossing between the two triangles in a graph requires $CD$, with at least one edge before it and one after it. Thus $d(A,F)=3$ in graph distance, regardless of the drawing.^[ref]

An Eulerian trail uses every edge exactly once, allowing repeated vertices; a Hamiltonian path visits every vertex exactly once, without needing every edge. Here $C,A,B,C,D,E,F,D$ is an Eulerian trail of length seven, whereas $A,B,C,D,E,F$ is a Hamiltonian path of length five. The first repeats $C,D$; the second omits $AC,DF$. Calling an Eulerian trail an "Eulerian path" must not be taken to impose our graph path convention.^[ref]

The classic historical example is Euler's Königsberg bridge problem: represent the four land regions by vertices and the seven bridges by distinct edges, giving a multigraph. Their degrees of vertices are $3,3,3,5$. In a trail using every edge, arrivals and departures pair at every internal vertex, so at most the two endpoints can have odd vertex degree. Four odd degrees of vertices therefore rule out such a route. This early foundational example of graph theory needs neither bridge lengths nor a geographical map. Euler's paper is conventionally associated with 1736, the volume's nominal year; the Euler Archive records its writing in 1735 and publication in 1741.^[ref][ref]

For a function $f:A\to B$, its graph is the subset $\{(x,f(x)):x\in A\}$ of $A\times B$. It differs from a graph in graph theory. A solution curve of a scalar differential equation can be a graph over its independent coordinate.

Graph theory studies vertices and the edges connecting them: which graph paths exist, how efficiently we can travel, and which connections are indispensable. A road map becomes a graph when junctions are vertices and road segments are edges; a communications network uses routers and links instead. The drawing helps us see connections, but its apparent lengths need not be the costs being optimized. Record travel times or transmission costs separately in a weighted graph.

The appeal of graph theory is that one model serves many practical questions. Graph paths describe routes, connected components of a graph identify isolated regions, and minimum spanning trees connect all locations cheaply. The shortest path problem asks a different question: find the cheapest journey between specified locations. Our four-vertex example below makes that distinction concrete and shows how the Dijkstra algorithm discovers a route without trying every graph path.

A mathematician, statistician and computer scientist who worked in combinatorics and graph theory.^[ref]

A physicist who studies networks using graph theory.^[ref]

Homepage: websites.umich.edu/~mejn/

A tree is a nonempty connected graph with no graph cycle. A leaf of a tree is a vertex of vertex degree one. As usual in this finite graph theory problem, trees are finite; the assertion would fail for a doubly infinite graph path.

Take a longest path $v_0,v_1,\dots ,v_m$ in a tree with at least two vertices. Connectedness gives $m\ge 1$. If $v_0$ had a neighbour outside this graph path, the path could be extended, contradicting maximal length. If it had a neighbour $v_j$ with $j\ge 2$, that edge together with the segment $v_0,v_1,\dots ,v_j$ would make a graph cycle. Thus its only neighbour is $v_1$, and it is a leaf of a tree. The same argument at the other end makes $v_m$ a second, distinct leaf of a tree. Hence

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.

A mathematician whose work includes graph theory and matroids; Tutte's theorem on perfect matchings is one of his fundamental results.

University of St Andrews biography and portrait collection.