Chromatic number Created 2026-09-24 Updated 2026-10-14
For a nonempty finite simple graph , the chromatic number is the least positive integer for which has a proper vertex graph coloring with at most colours. To establish an exact chromatic number, give a colouring using that many colours and explain why fewer cannot suffice. A colouring alone gives only an upper bound.[ref]
Figure 1.
Optimal graph colorings of the cycle graphs
. Numbers inside dots are colour labels, and labels outside are vertices. The cycle graph alternates two colours. The odd cycles need three, although is a triangle-free graph. Details.
These small graphs show why the chromatic number measures more than the presence of triangles in a graph:
For a cycle graph of even length, alternating two colours closes consistently; for an odd cycle, it does not, and a third colour on the final vertex suffices. More generally, the two-colourability criterion for bipartite graphs says that exactly when is a bipartite graph, and the odd-cycle characterization of bipartite graphs identifies the obstruction as an odd cycle. A nonempty graph with no edges has chromatic number one; a bipartite graph with at least one edge has chromatic number exactly two.
In the pairwise-conflict scheduling model of graph coloring, is the least number of interchangeable slots needed. This turns a drawing into an optimization question: how many groups of compatible activities are unavoidable? Greedy coloring constructs a valid grouping quickly, but its number of colours can exceed the chromatic number.
Crown graph Created 2026-10-14 Updated 2026-10-20
A crown graph is a complete bipartite graph with a perfect matching removed. For it has chromatic number two, but greedy coloring in the order uses colors: the matched pair gets the same new color, and every subsequent pair sees all earlier colors.
Greedy coloring 2026-10-14
For a finite simple graph, choose an order of its vertices and process them one at a time. Give each vertex the smallest positive colour label not already used by its coloured neighbours, and keep all earlier assignments. This is greedy coloring: each step makes a locally available choice. The resulting graph coloring is proper because, for each edge, its later endpoint avoids the colour of its earlier endpoint.[ref]
If the maximum degree of a graph is , a vertex has at most previously coloured neighbours, forbidding at most that many distinct colours. At least one label among remains available. Consequently every order gives
This is an upper bound, not a promise that greedy coloring attains the chromatic number.[ref]
Figure 1.
Two orders for greedy coloring of the same path graph
. The edges are in both drawings. Each dot shows its assigned colour number, with the vertex label above and its processing step below. The order uses two colours; the order uses three. Details.
Consider the path graph with edges . In the order , greedy coloring assigns the colours respectively. In the order , it assigns colours in processing order: vertices both get colour , then vertex gets colour , and finally vertex sees colours and at its two neighbours and must take colour . Written in vertex order , this second colouring is .
The path graph has chromatic number two: the first assignment achieves two colours, and any edge rules out one. Its maximum degree of a graph is two, so three colours in the second assignment still satisfy the bound. The extra colour comes entirely from the processing order. The crown graph gives a larger family where a poor order can make greedy coloring use arbitrarily many colours although the chromatic number remains two.
Replace by if necessary; this only weakens the hypothesis. We prove the result for finite graphs using a random proper partial graph coloring, then a deterministic completion. All constants below may depend on this fixed positive .
First record the extension of a partial colouring by neighbour-colour savings. If a partial colouring uses colours from a palette of size , let be the number of coloured neighbours of , their number of distinct colours, and the neighbour-colour saving. There are available colours and uncoloured neighbours. Thus
at every vertex suffices for greedy completion: each subsequently coloured neighbour removes at most one available colour while reducing the uncoloured-neighbour count by one.
Call high-degree if . Its graph neighbourhood has at least
nonadjacent pairs for all sufficiently large . Assign independently to every vertex one of tentative colours. Erase the colour of every vertex having a neighbour with the same tentative colour. The remaining colouring is proper; erasure is based on the original assignments and is not an iterative cascade.
For each , define as the number of tentative colours assigned to at least one nonadjacent pair in its graph neighbourhood. Let count those same colours for which at least one neighbour of loses its colour. Then counts repeated colours with no erased occurrence among the neighbours, so .
For a particular nonadjacent pair , consider the event that both receive one colour and no vertex of receives that colour. This guarantees that its exactly two occurrences in both survive. Its probability is at least
Here , , and for . Different pairs cannot be counted by the same colour in this event, since each event specifies exactly two occurrences in the graph neighbourhood. Therefore
for every high-degree vertex. This is the expected colour savings from sparse neighbourhoods.
We need simultaneous concentration, not just a positive expectation. A single tentative-colour change can affect or only in the old and new colour categories, so each function changes by at most two. A witness for fixes two vertices for each of colours. A witness for fixes such a nonadjacent pair and an erased neighbour together with a same-coloured adjacent vertex, at most four vertices per colour. Thus both are certifiable functions with Lipschitz constant and certificate parameter at most . Both are bounded by .
To see exactly what concentration follows, let an integer-valued change by at most per coordinate and have certificates of size at most . For an input with , fix such a certificate . If has , form a hybrid agreeing with that input on and with outside . The hybrid has value at least , so at least certificate positions differ from . Equal weights on show that its Talagrand convex distance from is at least . Talagrand's convex distance inequality therefore gives the two-threshold concentration for certifiable functions,
At an integer median , each median-side probability is at least . Taking thresholds or and using gives
for . Summing these tails gives . For sufficiently large , depending on , it follows that
Apply this to and . Except with probability at most , .
A bad event at depends only on tentative colours at distance at most two. It is independent of the entire collection of bad events at distance greater than four. Its dependency graph of events consequently has maximum degree at most for large . The Lovász local lemma now makes all high-degree vertices good simultaneously: exponential decay beats this polynomial dependency bound.
The needed local-lemma step can be checked directly. If every bad-event probability is at most , induction on conditioning sets gives : split those events into non-neighbours of , independent of it, and at most neighbours, whose avoidance probability is at least by the induction. Multiplying the resulting conditional avoidance probabilities makes total avoidance positive. Taking works whenever , since . Our bound satisfies this criterion once .
Choose a successful partial colouring and set . This contains the original palette. High-degree vertices satisfy
Low-degree vertices have after increasing if necessary. Greedy completion therefore gives . Finally, for ,
The constants are deliberately conservative; existence of a fixed positive improvement is the point. The same proof uses an external maximum degree bound, so if infinite locally finite graphs are allowed, every finite subgraph has this uniform colouring bound and compactness extends the colouring to the whole graph.
For the stronger triangle-free result, the appropriate brief route is semi-random palette refinement for triangle-free colouring. Start with colours and repeatedly make small tentative random assignments, retain conflict-free choices, and update the uncoloured vertices' available lists. Triangle-freeness makes each graph neighbourhood independent. Track list size and average residual neighbour congestion per colour, prune unusually congested colours, and use concentration inequalities and the Lovász local lemma to preserve these invariants through the rounds. The ratio of residual congestion to list size decreases until a final completion using the Lovász local lemma or greedy coloring succeeds. Average control and pruning are important: four-cycles are allowed, so assuming tight uniform concentration for every individual colour load would be unjustified. With a sufficiently large absolute , this programme gives . The one-round fixed-fraction saving proved above alone does not establish that logarithmic improvement.
To show the order is best possible, let through integers and sample with . A Chernoff bound and union bound give maximum degree at most with probability tending to one. The expected triangle count is at most , so fewer than vertices need be deleted to remove all triangles with high probability. For , the expected number of independent -sets is at most
its logarithm is at most , which tends to minus infinity. The altered triangle-free graph thus has at least vertices and independence number below , giving for an absolute positive . Append a disjoint star graph whose centre has vertex degree if needed to make its maximum degree exactly . This proves the triangle-free chromatic lower bound at bounded maximum degree, , so the preceding order cannot be improved in general.
The Brooks' theorem states that a finite connected simple graph with maximum vertex degree has chromatic number at most , unless it is a complete graph or an odd cycle graph; those exceptions have chromatic number .
First, if the graph is not regular, choose a vertex of degree less than and a spanning tree rooted at . Order vertices with every child before its parent, and apply the greedy coloring algorithm. Every vertex except has an as-yet uncolored parent, so has at most colored neighbors; also has fewer than neighbors. Thus colors suffice. This justifies reducing to the regular case.
Now let be regular, 3-connected and not complete. A shortest path between two nonadjacent vertices contains an induced two-edge path with nonadjacent. By 3-connectivity, is connected. Color with the same color. Choose a spanning tree of the remaining graph rooted at and color its vertices in child-before-parent order. Each vertex other than again has an uncolored parent and thus at most already colored neighbors. At the last vertex , the two neighbors share a color, so its neighbors use at most distinct colors. A color is therefore available at every step, proving in the required 3-connected noncomplete case. A 3-connected graph has , so the odd-cycle exception cannot arise here.
The first assertion is false. The complete graph has , but : color each of its three perfect matchings with its own color. It is connected and has more than two vertices.
The second assertion is false. Take the crown graph with bipartition and , joining to exactly when . Its chromatic number is two. In the ordering , the greedy coloring algorithm assigns color to both : both see all previously used colors, but they are not adjacent to each other. It therefore uses five colors, exceeding . Taking more pairs makes the ratio arbitrarily large.
The third assertion is true. When an edge is colored greedily, at most adjacent edges have previously received colors. Thus the greedy coloring algorithm uses at most colors whenever the graph has an edge. The edgeless case is trivial. This bound holds for every edge ordering.