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Pseudorandom graph

Pseudorandom 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 Pseudorandom graph rather than just read about it. In short: In graph theory, a graph is said to be a pseudorandom graph if it obeys certain properties that random graphs obey with high probability. There is no concrete definition of graph pseudorandomness, but there are many reasonable characterizations of pseudorandomness one can consider.

Key takeaways

  • Pseudorandom 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 Pseudorandom graph to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Pseudorandom graph from memory before moving on to harder problems.

Reference excerpt

In graph theory, a graph is said to be a pseudorandom graph if it obeys certain properties that random graphs obey with high probability. There is no concrete definition of graph pseudorandomness, but there are many reasonable characterizations of pseudorandomness one can consider. Pseudorandom properties were first formally considered by Andrew Thomason in 1987. He defined a condition called "jumbledness": a graph G = ( V , E ) {\displaystyle G=(V,E)} is said to be ( p , α ) {\displaystyle (p,\alpha )} -jumbled for real p {\displaystyle p} and α {\displaystyle \alpha } with 0 < p < 1 ≤ α {\displaystyle 0<p<1\leq \alpha } if

| e ( U ) − p ( | U | 2 ) | ≤ α | U | {\displaystyle \left|e(U)-p{\binom {|U|}{2}}\right|\leq \alpha |U|}

for every subset U {\displaystyle U} of the vertex set V {\displaystyle V} , where e ( U ) {\displaystyle e(U)} is the number of edges among U {\displaystyle U} (equivalently, the number of edges in the subgraph induced by the vertex set U {\displaystyle U} ). It can be shown that the Erdős–Rényi random graph G ( n , p ) {\displaystyle G(n,p)} is almost surely ( p , O ( n p ) ) {\displaystyle (p,O({\sqrt {np}}))} -jumbled. However, graphs with less uniformly distributed edges, for example a graph on 2 n {\displaystyle 2n} vertices consisting of an n {\displaystyle n} -vertex complete graph and n {\displaystyle n} completely independent vertices, are not ( p , α ) {\displaystyle (p,\alpha )} -jumbled for any small α {\displaystyle \alpha } , making jumbledness a reasonable quantifier for "random-like" properties of a graph's edge distribution.

Connection to local conditions Thomason showed that the "jumbled" condition is implied by a simpler-to-check condition, only depending on the codegree of two vertices and not every subset of the vertex set of the graph. Letting codeg ⁡ ( u , v ) {\displaystyle \operatorname {codeg} (u,v)} be the number of common neighbors of two vertices u {\displaystyle u} and v {\displaystyle v} , Thomason showed that, given a graph G {\displaystyle G} on n {\displaystyle n} vertices with minimum degree n p {\displaystyle np} , if codeg ⁡ ( u , v ) ≤ n p 2 + ℓ {\displaystyle \operatorname {codeg} (u,v)\leq np^{2}+\ell } for every u {\displaystyle u} and v {\displaystyle v} , then G {\displaystyle G} is ( p , ( p + ℓ ) n ) {\displaystyle \left(p,{\sqrt {(p+\ell )n}}\,\right)} -jumbled. This result shows how to check the jumbledness condition algorithmically in polynomial time in the number of vertices, and can be used to show pseudorandomness of specific graphs.

Chung–Graham–Wilson theorem In the spirit of the conditions considered by Thomason and their alternately global and local nature, several weaker conditions were considered by Chung, Graham, and Wilson in 1989: a graph G {\displaystyle G} on n {\displaystyle n} vertices with edge density p {\displaystyle p} and some ε > 0 {\displaystyle \varepsilon >0} can satisfy each of these conditions if

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Pseudorandom graph

Start with the simplest possible case. Write down what Pseudorandom 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 Pseudorandom 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 Pseudorandom 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 Pseudorandom graph

In research
Pseudorandom 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 Pseudorandom 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
Pseudorandom graph is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph theory, so understanding it makes those chapters shorter.
In everyday life
Look for Pseudorandom 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 Pseudorandom graph in 20 minutes

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

Frequently asked questions

What is Pseudorandom graph in simple terms?

In graph theory, a graph is said to be a pseudorandom graph if it obeys certain properties that random graphs obey with high probability. There is no concrete definition of graph pseudorandomness, but there are many reasonable characterizations of pseudorandomness one can consider.

Why does Pseudorandom 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 Pseudorandom 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 Pseudorandom graph.

Tags

  • Graph theory

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