How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
The expected number of triangles in is
Example
Let be the number of triangles in . Then and
Facts & Assumptions
Given: The random graph and its triangle count .
has independent Bernoulli edge coordinates (The Erdős-Rényi finite random graph ).
A prescribed set of present edges has probability (A prescribed set of present and absent edges in has product probability).
Indicators count occurrences and expectation is linear (Indicators turn event probabilities, intersections, and finite counts into expectations and products, Expectation is linear for every finite family of random variables, without any independence hypothesis).
There are vertex triples and vertex pairs (The set of -element subsets and the binomial coefficient ).
Variance and covariance obey their centred-product definitions, and the variance of a finite sum is the sum of individual variances plus twice the unordered pairwise covariances (Variance, standard deviation, and covariance on a finite probability space, Variance of a finite sum as the sum of all variances and covariances).
Verification
Write , with ranging over three-element vertex sets. Each requires three present edges and has expectation , so .
Each indicator has variance . Two distinct triangles are independent unless they share an edge; if they share an edge, their joint occurrence requires five edges and their covariance is .
An unordered pair of triangles sharing an edge is determined by the common edge and the two distinct extra vertices, so there are such pairs.
Sum the individual variances and twice the unordered covariances to obtain the formula. For all relevant binomial coefficients vanish; for the variance is zero.
Depends on
- The Erdős-Rényi finite random graph $G(n,p)$
- A prescribed set of present and absent edges in $G(n,p)$ has product probability
- Indicators turn event probabilities, intersections, and finite counts into expectations and products
- Expectation is linear for every finite family of random variables, without any independence hypothesis
- The set $[A]^{k}$ of $k$-element subsets and the binomial coefficient $\binom{n}{k} := \lvert [n]^{k}\rvert$
- Variance, standard deviation, and covariance on a finite probability space
- Variance of a finite sum as the sum of all variances and covariances
Used by
Nothing in the library uses this result yet.
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 84 results over 22 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- J. Matousek and J. Vondrak, The Probabilistic Method, Chapters 1 and 3 (standard reference, not scraped)