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The independence number is the largest cardinality of an independent set in a finite simple graph:It answers "how many mutually compatible objects can be selected at once?", while the chromatic number asks how many compatible groups are needed for all the vertices. Diestel uses this notation for a largest independent set.^[ref]

For the cycle graph $C_5$, the independent set $\{0,2\}$ shows that $\alpha (C_5)\ge 2$. Three selected vertices would require at least three unselected vertices separating them around the cycle graph, which has only five vertices. Thus $\alpha (C_5)=2$. There are five largest independent sets, obtained by rotating $\{0,2\}$ around the cycle graph, and five corresponding minimum vertex covers.

For the complete bipartite graph $K_{2,3}$, an independent set cannot contain a vertex from each side, since every such pair is joined by an edge. The larger side has three vertices, so $\alpha (K_{2,3})=3$. A triangle in a graph has independence number one. A graph with no edges and $n$ vertices has independence number $n$, including $\alpha (G)=0$ when $n=0$.

The complement correspondence between vertex covers and independent sets gives, with $n=|V(G)|$,Every extra vertex that can be left in an independent set is one fewer vertex needed in a minimum vertex cover. This identity holds for all finite simple graphs, including the triangle in a graph and the cycle graph $C_5$; it does not require the bipartite graph hypothesis of Kőnig's theorem for bipartite matching.

For a nonempty graph, every colour class of a proper vertex colouring contains at most $\alpha (G)$ vertices. Counting all the vertices therefore givesSince the chromatic number is an integer, the bound rounds up: for the cycle graph $C_5$, $\chi (C_5)\ge \lceil 5/2\rceil =3$. It is attained by assigning colour $A$ to $0,2$, colour $B$ to $1,3$, and colour $C$ to $4$. All five edges, including $40$, have differently coloured endpoints.

An independent set selects mutually compatible objects when an edge of a graph represents a conflict. For example, if vertices are activities and edges join activities that cannot occur together, an independent set is a collection that can share one time slot. Finding the largest such collection is measured by the independence number.^[ref]

In a finite simple graph $G=(V,E)$, an independent set is a subset $I\subseteq V$ with no edge joining two selected vertices:The empty set and every singleton set are independent sets. In the cycle graph $C_5$ drawn under vertex cover, $\{0,2\}$ is an independent set, but $\{0,4\}$ is not: the closing edge $40$ joins those two vertices. Choosing nonconsecutive labels is insufficient unless the wraparound edge is checked.

An independent set of maximum size has cardinality equal to the independence number. Being unable to add another vertex only makes an independent set inclusion-maximal. In the star graph $K_{1,3}$, the centre alone is inclusion-maximal, since it is adjacent to every leaf, but the three leaves form a larger independent set. This distinction mirrors the difference between an inclusion-minimal vertex cover and a minimum vertex cover.

Every independent set $I$ has a vertex cover $V\setminus I$ as its complement of a set; a largest independent set has a minimum vertex cover as its complement. In every proper vertex colouring, each colour class is an independent set, so graph coloring partitions the vertices into mutually compatible groups.

A minimum vertex cover is a vertex cover of smallest cardinality among all vertex covers of the same finite simple graph. The optimum size is the vertex cover number. By the complement of a set correspondence, a minimum vertex cover leaves a largest possible independent set unselected.

Minimum size is stronger than merely being unable to remove a selected vertex. In the star graph $K_{1,3}$, the three leaves form a vertex cover from which no vertex can be removed: each leaf covers its own edge. Nevertheless, the centre alone is a minimum vertex cover of size one. Such an inclusion-minimal vertex cover can therefore be much larger than a minimum vertex cover.

The vertex cover decision problem takes a finite simple graph $G$ and a nonnegative integer $k$, and asks whether there is a vertex cover of cardinality at most $k$. The optimization problem asks for a minimum vertex cover, with cost equal to its cardinality.

For the decision problem, if there are no edges, accept; if there is an edge but $k=0$, reject. Otherwise choose an edge $uv$ and branch: either include $u$ and recurse on $G-u$ with budget $k-1$, or include $v$ and recurse on $G-v$ with budget $k-1$. Accept if either branch accepts. Every vertex cover contains at least one endpoint of $uv$, so every possible solution is retained by at least one branch. Conversely, adding the selected endpoint to a vertex cover returned by the smaller instance covers all original edges. This proves correctness by induction on the remaining budget.

The search tree has depth at most $k$ and at most $2^k$ leaves. Processing each node in $O(|V|+|E|)$ time gives $O(2^k(|V|+|E|))$ running time, establishing fixed-parameter tractability.

For optimization, greedily build a maximal matching $M$: repeatedly select an edge and discard every edge touching either endpoint. Return the set $C$ of all endpoints of $M$. Any edge with neither endpoint in $C$ could be added to $M$, contradicting maximality; thus $C$ is a vertex cover. Since the edges of a matching in a graph have pairwise disjoint endpoint sets, every vertex cover must contain at least one different vertex for each of them. ThereforeThe greedy construction takes polynomial time, and can be implemented in $O(|V|+|E|)$ with adjacency lists. It needs only a maximal matching, not a maximum matching.

Choose a maximum matching $M$ and a minimum vertex cover $C$. By Kőnig's theorem for bipartite matching, $|M|=|C|$. Every edge of $M$ must have at least one endpoint in $C$, because $C$ is a vertex cover. Distinct edges of the matching in a graph have disjoint endpoints, so these requirements already use at least $|M|$ distinct vertices of $C$.

Since $C$ has exactly that many vertices, each edge of $M$ has exactly one endpoint in $C$, and no vertex of $C$ lies outside the endpoints of $M$. Thus selecting the unique endpoint in $C$ from each edge of $M$ produces precisely the vertex cover $C$:The argument also covers an edgeless graph: take both sets empty.

A vertex cover chooses vertices that collectively touch every edge of a graph. Think of placing monitors at junctions: if a monitor watches every connection incident to its junction, a vertex cover watches all connections. The minimum vertex cover asks how few monitors suffice. This is a model of covering connections through their endpoints, rather than of monitoring distances in the drawing.

For a finite simple graph $G=(V,E)$, a vertex cover is a subset $C\subseteq V$ satisfyingBoth endpoints may be selected; the condition requires at least one. An isolated vertex covers no edge, so a minimum vertex cover never needs it.^[ref]

Take the cycle graph $C_5$ with vertices $0,1,2,3,4$ and edges $01,12,23,34,40$. The set $C=\{1,3,4\}$ is a vertex cover: vertex $1$ covers $01$ and $12$, vertex $3$ covers $23$ and $34$, and vertex $4$ covers $40$ as well as $34$. In particular, the closing edge $40$ is covered even though vertex $0$ is unselected.

The unselected vertices $I=V\setminus C=\{0,2\}$ form an independent set. This is the key connection: an edge missed by $C$ would have both endpoints in $I$, precisely what an independent set forbids. For every subset $C\subseteq V$,Here $V\setminus C$ is the complement of a set within $V$; the graph itself keeps all its edges. The complement graph is a different construction.^[ref]

The endpoints of any maximal matching form a vertex cover: an edge avoiding all endpoints would enlarge that matching in a graph. Consequently a graph with no matching in a graph of size $s$ has a vertex cover of size at most $2(s-1)$. This supplies a convenient vertex cover, but it need not be a minimum vertex cover.

The vertex cover number records the size of a minimum vertex cover:The vertex cover number is a number, while a minimum vertex cover is a set attaining it. For the cycle graph $C_5$, the independence number is $2$, so the complement correspondence gives $\tau (C_5)=5-2=3$. The vertex cover $\{1,3,4\}$ above attains this size.

Every matching in a graph gives a lower bound on the vertex cover number. Its edges have no shared endpoints, so covering each selected edge requires a different vertex. Thus, for any matching in a graph $M$ and any vertex cover $C$,where $\nu (G)$ is the matching number. Exhibiting a matching in a graph and a vertex cover of the same size certifies that both are optimum. Kőnig's theorem for bipartite matching says that every finite bipartite graph admits this equality: $\nu (G)=\tau (G)$.^[ref]

For the complete bipartite graph $K_{2,3}$, let the left vertices be $L=\{a,b\}$ and the right vertices be $R=\{x,y,z\}$. All six edges join $L$ to $R$. Selecting $C=L$ covers every edge; selecting $M=\{ax,by\}$ gives a matching in a graph of size two. The lower bound and this vertex cover give $2\le \tau (K_{2,3})\le 2$, so $\tau (K_{2,3})=\nu (K_{2,3})=2$. The remaining vertices $R$ form a largest independent set of size three. Crossings of drawn edges are not extra vertices.

The bipartite graph hypothesis matters. In a triangle in a graph, any two vertices form a minimum vertex cover, while any two edges share an endpoint. Hence $\tau (K_3)=2$ and $\nu (K_3)=1$. Similarly, the cycle graph $C_5$ has $\tau (C_5)=3$ and $\nu (C_5)=2$. The inequality for the matching number remains valid, but equality can fail.