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Perfect graph

Perfect graph is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Perfect graph rather than just read about it. In short: In graph theory, a perfect graph is a graph in which the chromatic number equals the size of the maximum clique, both in the graph itself and in every induced subgraph. In all graphs, the chromatic number is greater than or equal to the size of the maximum clique, but they can be far apart.

Perfect graph — main illustration
Perfect graph — illustration

Key takeaways

  • Perfect graph belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Perfect graph to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Perfect graph from memory before moving on to harder problems.

Reference excerpt

In graph theory, a perfect graph is a graph in which the chromatic number equals the size of the maximum clique, both in the graph itself and in every induced subgraph. In all graphs, the chromatic number is greater than or equal to the size of the maximum clique, but they can be far apart. A graph is perfect when these numbers are equal, and remain equal after the deletion of arbitrary subsets of vertices. The perfect graphs include many important families of graphs and serve to unify results relating colorings and cliques in those families. For instance, in all perfect graphs, the graph coloring problem, maximum clique problem, and maximum independent set problem can all be solved in polynomial time, despite their greater complexity for non-perfect graphs. In addition, several important minimax theorems in combinatorics, including Dilworth's theorem and Mirsky's theorem on partially ordered sets, Kőnig's theorem on matchings, and the Erdős–Szekeres theorem on monotonic sequences, can be expressed in terms of the perfection of certain associated graphs. The perfect graph theorem states that the complement graph of a perfect graph is also perfect. The strong perfect graph theorem characterizes the perfect graphs in terms of certain forbidden induced subgraphs, leading to a polynomial time algorithm for testing whether a graph is perfect.

Definitions and characterizations

A clique in an undirected graph is a subset of its vertices that are all adjacent to each other, such as the subsets of vertices connected by heavy edges in the illustration. The clique number is the number of vertices in the largest clique: two in the illustrated seven-vertex cycle, and three in the other graph shown. A graph coloring assigns a color to each vertex so that each two adjacent vertices have different colors, also shown in the illustration. The chromatic number of a graph is the minimum number of colors in any coloring. The colorings shown are optimal, so the chromatic number is three for the 7-cycle and four for the other graph shown. The vertices of any clique must have different colors, so the chromatic number is always greater than or equal to the clique number. For some graphs, they are equal; for others, such as the ones shown, they are unequal. The perfect graphs are defined as the graphs for which these two numbers are equal, not just in the graph itself, but in every induced subgraph obtained by deleting some of its vertices.

The perfect graph theorem asserts that the complement graph of a perfect graph is itself perfect. The complement graph has an edge between two vertices if and only if the given graph does not. A clique, in the complement graph, corresponds to an independent set in the given. A coloring of the complement graph corresponds to a clique cover, a partition of the vertices of the given graph into cliques. The fact that the complement of a perfect graph G {\displaystyle G} is also perfect implies that, in G {\displaystyle G} itself, the independence number (the size of its maximum independent set), equals its clique cover number (the fewest number of cliques needed in a clique cover). More strongly, the same thing is true in every induced subgraph of the complement graph. This provides an alternative and equivalent definition of the perfect graphs: they are the graphs for which, in each induced subgraph, the independence number equals the clique cover number. The strong perfect graph theorem gives a different way of defining perfect graphs, by their structure instead of by their properties. It is based on the existence of cycle graphs and their complements within a given graph. A cycle of odd length, greater than three, is not perfect: its clique number is two, but its chromatic number is three. By the perfect graph theorem, the complement of an odd cycle of length greater than three is also not perfect. The complement of a length-5 cycle is another length-5 cycle, but for larger odd lengths the complement is not a cycle; it is called an anticycle. The strong perfect graph theorem asserts that these are the only forbidden induced subgraphs for the perfect graphs: a graph is perfect if and only if its induced subgraphs include neither an odd cycle nor an odd anticycle of five or more vertices. In this context, induced cycles that are not triangles are called "holes", and their complements are called "antiholes", so the strong perfect graph theorem can be stated more succinctly: a graph is perfect if and only if it has neither an odd hole nor an odd antihole. These results can be combined in another characterization of perfect graphs: they are the graphs for which the product of the clique number and independence number is greater than or equal to the number of vertices, and for which the same is true for all induced subgraphs. Because the statement of this characterization remains invariant under complementation of graphs, it implies the perfect graph theorem. One direction of this characterization follows easily from the original definition of perfect: the number of vertices in any graph equals the sum of the sizes of the color classes in an optimal coloring, and is less than or equal to the number of colors multiplied by the independence number. In a perfect graph, the number of colors equals the clique number, and can be replaced by the clique number in this inequality. The other direction can be proved directly, but it also follows from the strong perfect graph theorem: if a graph is not perfect, it contains an odd cycle or its complement, and in these subgraphs the product of the clique number and independence number is one less than the number of vertices.

… excerpt ends here. Continue reading the full article.

Illustrations

Perfect graph: The graph of the 3-3 duoprism (the line graph of 
  
    
      
        
          K
          
            3
            ,
            3
          
        
      
    
    {\displaystyle K_{3,3}}
  
) is perfect. Here it is colored with three colors, with one of its 3-vertex maximum cliques highlighted.
The graph of the 3-3 duoprism (the line graph of K 3 , 3 {\displaystyle K_{3,3}} ) is perfect. Here it is colored with three colors, with one of its 3-vertex maximum cliques highlighted.
Perfect graph: A seven-vertex cycle and its complement, showing in each case an optimal coloring and a maximum clique (shown with heavy edges). Neither graph uses a number of colors equal to its clique size, so neither is perfect.
A seven-vertex cycle and its complement, showing in each case an optimal coloring and a maximum clique (shown with heavy edges). Neither graph uses a number of colors equal to its clique size, so neither is perfect.
Perfect graph: Two complementary perfect graphs
Two complementary perfect graphs
Perfect graph: A bipartite graph (left) and its line graph (right). The shaded cliques in the line graph correspond to the vertices of the underlying bipartite graph, and have size equal to the degree of the corresponding vertex.
A bipartite graph (left) and its line graph (right). The shaded cliques in the line graph correspond to the vertices of the underlying bipartite graph, and have size equal to the degree of the corresponding vertex.
Perfect graph: A line perfect graph, with black bipartite biconnected components, blue 
  
    
      
        
          K
          
            4
          
        
      
    
    {\displaystyle K_{4}}
  
, and red triangular books
A line perfect graph, with black bipartite biconnected components, blue K 4 {\displaystyle K_{4}} , and red triangular books

Worked examples

Example 1 — a first encounter with Perfect graph

Start with the simplest possible case. Write down what Perfect graph claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Perfect graph before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Perfect graph ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Perfect graph

In research
Perfect graph appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Perfect graph in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Perfect graph is common in secondary-school and first-year university syllabi. It links to neighbouring topics Perfect graphs, so understanding it makes those chapters shorter.
In everyday life
Look for Perfect graph outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Perfect graph in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Perfect graph means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Perfect graph out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Perfect graph in simple terms?

In graph theory, a perfect graph is a graph in which the chromatic number equals the size of the maximum clique, both in the graph itself and in every induced subgraph. In all graphs, the chromatic number is greater than or equal to the size of the maximum clique, but they can be far apart.

Why does Perfect graph matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Perfect graph?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Perfect graph.

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