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Sidorenko's conjecture

Sidorenko's conjecture 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 Sidorenko's conjecture rather than just read about it. In short: Sidorenko's conjecture is a major conjecture in the field of extremal graph theory, posed by Alexander Sidorenko in 1986. Roughly speaking, the conjecture states that for any bipartite graph H {\displaystyle H} and graph G {\displaystyle G} on n {\displaystyle n} vertices with average degree p n {\displaystyle pn} , there are at least p | E ( H ) | n | V ( H ) | {\displaystyle p^{|E(H)|}n^{|V(H)|}} labeled copies of…

Key takeaways

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

Reference excerpt

Sidorenko's conjecture is a major conjecture in the field of extremal graph theory, posed by Alexander Sidorenko in 1986. Roughly speaking, the conjecture states that for any bipartite graph H {\displaystyle H} and graph G {\displaystyle G} on n {\displaystyle n} vertices with average degree p n {\displaystyle pn} , there are at least p | E ( H ) | n | V ( H ) | {\displaystyle p^{|E(H)|}n^{|V(H)|}} labeled copies of H {\displaystyle H} in G {\displaystyle G} , up to a small error term. Formally, it provides an intuitive inequality about graph homomorphism densities in graphons. The conjectured inequality can be interpreted as a statement that the density of copies of H {\displaystyle H} in a graph is asymptotically minimized by a random graph, as one would expect a p | E ( H ) | {\displaystyle p^{|E(H)|}} fraction of possible subgraphs to be a copy of H {\displaystyle H} if each edge exists with probability p {\displaystyle p} .

Statement Let H {\displaystyle H} be a graph. Then H {\displaystyle H} is said to have Sidorenko's property if, for all graphons W {\displaystyle W} , the inequality

t ( H , W ) ≥ t ( K 2 , W ) | E ( H ) | {\displaystyle t(H,W)\geq t(K_{2},W)^{|E(H)|}}

is true, where t ( H , W ) {\displaystyle t(H,W)} is the homomorphism density of H {\displaystyle H} in W {\displaystyle W} . Sidorenko's conjecture (1986) states that every bipartite graph has Sidorenko's property. If W {\displaystyle W} is a graph G {\displaystyle G} , this means that the probability of a uniform random mapping from V ( H ) {\displaystyle V(H)} to V ( G ) {\displaystyle V(G)} being a homomorphism is at least the product over each edge in H {\displaystyle H} of the probability of that edge being mapped to an edge in G {\displaystyle G} . This roughly means that a randomly chosen graph with fixed number of vertices and average degree has the minimum number of labeled copies of H {\displaystyle H} . This is not a surprising conjecture because the right hand side of the inequality is the probability of the mapping being a homomorphism if each edge map is independent. So one should expect the two sides to be at least of the same order. The natural extension to graphons would follow from the fact that every graphon is the limit point of some sequence of graphs. The requirement that H {\displaystyle H} is bipartite to have Sidorenko's property is necessary — if W {\displaystyle W} is a bipartite graph, then t ( K 3 , W ) = 0 {\displaystyle t(K_{3},W)=0} since W {\displaystyle W} is triangle-free. But t ( K 2 , W ) {\displaystyle t(K_{2},W)} is twice the number of edges in W {\displaystyle W} , so Sidorenko's property does not hold for K 3 {\displaystyle K_{3}} . A similar argument shows that no graph with an odd cycle has Sidorenko's property. Since a graph is bipartite if and only if it has no odd cycles, this implies that the only possible graphs that can have Sidorenko's property are bipartite graphs.

Equivalent formulation Sidorenko's property is equivalent to the following reformulation:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Sidorenko's conjecture

Start with the simplest possible case. Write down what Sidorenko's conjecture 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 Sidorenko's conjecture 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 Sidorenko's conjecture 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 Sidorenko's conjecture

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

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

Frequently asked questions

What is Sidorenko's conjecture in simple terms?

Sidorenko's conjecture is a major conjecture in the field of extremal graph theory, posed by Alexander Sidorenko in 1986. Roughly speaking, the conjecture states that for any bipartite graph H {\displaystyle H} and graph G {\displaystyle G} on n {\displaystyle n} vertices with average degree p n {\…

Why does Sidorenko's conjecture 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 Sidorenko's conjecture?

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 Sidorenko's conjecture.

Tags

  • Conjectures
  • Statements in graph theory

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