Strongly regular graphs #
Main definitions #
G.IsSRGWith n k ℓ μ(seeSimpleGraph.IsSRGWith) is a structure for aSimpleGraphsatisfying the following conditions:- The cardinality of the vertex set is
n Gis a regular graph with degreek- The number of common neighbors between any two adjacent vertices in
Gisℓ - The number of common neighbors between any two nonadjacent vertices in
Gisμ
- The cardinality of the vertex set is
Main theorems #
IsSRGWith.compl: the complement of a strongly regular graph is strongly regular.IsSRGWith.param_eq:k * (k - ℓ - 1) = (n - k - 1) * μwhen0 < n.IsSRGWith.matrix_eq: letAandCbeG's andGᶜ's adjacency matrices respectively andIbe the identity matrix, thenA ^ 2 = k • I + ℓ • A + μ • C.
A graph is strongly regular with parameters n k ℓ μ if
- its vertex set has cardinality
n - it is regular with degree
k - every pair of adjacent vertices has
ℓcommon neighbors - every pair of nonadjacent vertices has
μcommon neighbors
- regular : G.IsRegularOfDegree k
- of_adj (v w : V) : G.Adj v w → Fintype.card ↑(G.commonNeighbors v w) = ℓ
- of_not_adj : Pairwise fun (v w : V) => ¬G.Adj v w → Fintype.card ↑(G.commonNeighbors v w) = μ
Instances For
Empty graphs are strongly regular. Note that ℓ can take any value
for empty graphs, since there are no pairs of adjacent vertices.
Complete graphs are strongly regular. Note that μ can take any value
for complete graphs, since there are no distinct pairs of non-adjacent vertices.
Assuming G is strongly regular, 2*(k + 1) - m in G is the number of vertices that are
adjacent to either v or w when ¬G.Adj v w. So it's the cardinality of
G.neighborSet v ∪ G.neighborSet w.
The parameters of a strongly regular graph with at least one vertex satisfy
k * (k - ℓ - 1) = (n - k - 1) * μ.
Let A and C be the adjacency matrices of a strongly regular graph with parameters n k ℓ μ
and its complement respectively and I be the identity matrix,
then A ^ 2 = k • I + ℓ • A + μ • C. C is equivalent to the expression J - I - A
more often found in the literature, where J is the all-ones matrix.