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Graphs with few cliques

Graphs with few cliques is a mathematics 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 Graphs with few cliques rather than just read about it. In short: In graph theory, a class of graphs is said to have few cliques if every member of the class has a polynomial number of maximal cliques. Certain generally NP-hard computational problems are solvable in polynomial time on such classes of graphs, making graphs with few cliques of interest in computational graph theory, network analysis, and other branches of applied mathematics.

Key takeaways

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

Reference excerpt

In graph theory, a class of graphs is said to have few cliques if every member of the class has a polynomial number of maximal cliques. Certain generally NP-hard computational problems are solvable in polynomial time on such classes of graphs, making graphs with few cliques of interest in computational graph theory, network analysis, and other branches of applied mathematics. Informally, a family of graphs has few cliques if the graphs do not have a large number of large clusters.

Definition A clique of a graph is a complete subgraph, while a maximal clique is a clique that is not properly contained in another clique. One can regard a clique as a cluster of vertices, since they are by definition all connected to each other by an edge. The concept of clusters is ubiquitous in data analysis, such as on the analysis of social networks. For that reason, limiting the number of possible maximal cliques has computational ramifications for algorithms on graphs or networks. Formally, let X {\displaystyle X} be a class of graphs. If for every n {\displaystyle n} -vertex graph G {\displaystyle G} in X {\displaystyle X} , there exists a polynomial f ( n ) {\displaystyle f(n)} such that G {\displaystyle G} has O ( f ( n ) ) {\displaystyle O(f(n))} maximal cliques, then X {\displaystyle X} is said to be a class of graphs with few cliques.

Examples The Turán graph T ( n , ⌈ n / 3 ⌉ ) {\displaystyle T(n,\lceil n/3\rceil )} has an exponential number of maximal cliques. In particular, this graph has exactly 3 n / 3 {\displaystyle 3^{n/3}} maximal cliques when n ≡ 0 mod 3 {\displaystyle n\equiv 0\mod 3} , which is asymptotically greater than any polynomial function. This graph is sometimes called the Moon-Moser graph, after Moon & Moser showed in 1965 that this graph has the largest number of maximal cliques among all graphs on n {\displaystyle n} vertices. So the class of Turán graphs does not have few cliques. A tree T {\displaystyle T} on n {\displaystyle n} vertices has as many maximal cliques as edges, since it contains no triangles by definition. Any tree has exactly n − 1 {\displaystyle n-1} edges, and therefore that number of maximal cliques. So the class of trees has few cliques. A chordal graph on n {\displaystyle n} vertices has at most n {\displaystyle n} maximal cliques, so chordal graphs have few cliques. Any planar graph on n {\displaystyle n} vertices has at most 8 n − 16 {\displaystyle 8n-16} maximal cliques, so the class of planar graphs has few cliques. Any n {\displaystyle n} -vertex graph with boxicity b {\displaystyle b} has O ( n b ) {\displaystyle O(n^{b})} maximal cliques, so the class of graphs with bounded boxicity has few cliques. Any n {\displaystyle n} -vertex graph with degeneracy d {\displaystyle d} has at most ( n − d ) 3 d / 3 {\textstyle (n-d)3^{d/3}} maximal cliques whenever d ≡ 0 mod 3 {\displaystyle d\equiv 0\mod 3} and n ≥ d + 3 {\displaystyle n\geq d+3} , so the class of graphs with bounded degeneracy has few cliques. Let G {\displaystyle G} be an intersection graph of n {\displaystyle n} convex polytopes in d {\displaystyle d} -dimensional Euclidean space whose facets are parallel to k {\displaystyle k} hyperplanes. Then the number of maximal cliques of G {\displaystyle G} is O ( n d k d + 1 ) {\textstyle O\left(n^{dk^{d+1}}\right)} , which is polynomial in n {\displaystyle n} for fixed d {\displaystyle d} and k {\displaystyle k} . Therefore, the class of intersection graphs of convex polytopes in fixed-dimensional Euclidean space with a bounded number of facets has few cliques.

References

Worked examples

Example 1 — a first encounter with Graphs with few cliques

Start with the simplest possible case. Write down what Graphs with few cliques claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, 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 Graphs with few cliques 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 Graphs with few cliques 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 Graphs with few cliques

In research
Graphs with few cliques appears in mathematics 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 Graphs with few cliques 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
Graphs with few cliques is common in secondary-school and first-year university syllabi. It links to neighbouring topics Discrete mathematics, Graphs, so understanding it makes those chapters shorter.
In everyday life
Look for Graphs with few cliques 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 Graphs with few cliques in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Graphs with few cliques 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 Graphs with few cliques out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Graphs with few cliques in simple terms?

In graph theory, a class of graphs is said to have few cliques if every member of the class has a polynomial number of maximal cliques. Certain generally NP-hard computational problems are solvable in polynomial time on such classes of graphs, making graphs with few cliques of interest in computati…

Why does Graphs with few cliques matter?

Because it connects several mathematics 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 Graphs with few cliques?

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 Graphs with few cliques.

Tags

  • Discrete mathematics
  • Graphs

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