False twin 2026-10-07
Distinct vertices with equal open neighbourhoods are false twins. They are nonadjacent: adjacency would put one vertex in the other's open neighbourhood but not its own. Equal open neighbourhoods define false-twin classes, which are independent sets and have uniform adjacency to every other such class.
For a fixed uniform hypergraph with vertices, uniformity , and at least one edge, define its Turán density by
where is the maximum edge count of an -free hypergraph. The limit exists: averaging the edge count over all -vertex subsets of an extremal -vertex hypergraph, with , shows that the displayed normalized extremal numbers are nonincreasing. Thus their limit is their infimum.
For hypergraph supersaturation by sampling, choose so that . The expected edge count in a uniform -vertex subset of is at least . A subset containing no copy of has at most edges, while any subset has at most . Hence at least an fraction of the subsets contain . Every particular copy of lies in exactly such subsets, so their number is at least
for . Take
For , the requested integer lower bound is zero; for , the calculation proves it. This positive depends only on and . If the density hypothesis is impossible there is nothing to prove. For an edgeless , every -set is already a copy and the conclusion follows directly.
For the edge-triangle objective, let be the open neighbourhood of and define its local score
It counts the contribution to of edges and triangles in a graph containing . If are nonadjacent, making a false twin of changes the objective by , since no edge or triangle in a graph uses both. More generally, partition vertices into false-twin classes, meaning equal open neighbourhoods. If distinct classes have no edges between them, making every vertex of a twin of a representative of changes the objective by . Choose the direction with nonnegative change.
Among graphs maximizing the objective, choose one maximizing the sum of squared false-twin class sizes. The operation cannot split any old false-twin class and merges , so it would strictly increase that secondary quantity. Therefore no two distinct classes can be nonadjacent. An objective maximizer is a complete multipartite graph. This edge-triangle symmetrization works for every real , with no assumption on its sign.
For positive part sizes , the correct multipartite formula is
The first sum in the printed intermediate formula has inconsistent indices; the edge count is the pairwise-product sum above. Fix two part sizes with sum and let be the sum of all other sizes. All terms varying with the pair have the form
Choose a maximizing partition with the fewest nonempty parts. If , merging those two parts cannot decrease and reduces the number of parts, a contradiction. Therefore this coefficient is positive for every pair. If two integer sizes differ by at least two, moving one vertex from the larger to the smaller increases their product and hence , again a contradiction. All sizes differ by at most one, so a maximizer is a Turan graph. This is balancing a multipartite edge-triangle objective.
Next maximize the same polynomial on the compact simplex of nonnegative real sizes summing to , starting with at least two possible parts. Choose a maximizer with the smallest positive support. The merging argument still applies, and now varying two positive unequal sizes towards equality improves the product. Thus every positive size is for some integer . A two-part choice has value when , so . The optimum is attained at rational sizes, and therefore
One may take the initial simplex to have coordinates, so it includes every multipartite graph obtained above; zero coordinates are discarded.
Set . Dividing the right side by gives
because is an integer at least two. Rearranging proves
Equality in the continuous calculation occurs at two or three equal parts, explaining the triangle support line between bipartite and tripartite Turan graphs.