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A graph $F$ is called a fractalizer if for all $n$ the only graphs which maximize the number of induced copies of $F$ on $n$ vertices are the balanced iterated blow ups of $F$. While the net graph is not a fractalizer, we show that the net is nearly a fractalizer. Let $N(n)$ be the maximum number of induced copies of the net graph among all graphs on $n$ vertices. For sufficiently large $n$ we show that, $N(n) = x_1cdot x_2 cdot x_3 cdot x_4 cdot x_5 cdot x_6 + N(x_1) + N(x_2) + N(x_3) + N(x_4) + N(x_5) + N(x_6)$ where $sigma x_i = n$ and all $x_i$ are as equal as possible. Furthermore, we show that the unique graph which maximizes $N(6^k)$ is the balanced iterated blow up of the net for $k$ sufficiently large. We expand on the standard flag algebra and stability techniques through more careful counting and numerical optimization techniques.
We present a sufficient condition for the stability property of extremal graph problems that can be solved via Zykovs symmetrisation. Our criterion is stated in terms of an analytic limit version of the problem. We show that, for example, it applies
A long standing open problem in extremal graph theory is to describe all graphs that maximize the number of induced copies of a path on four vertices. The character of the problem changes in the setting of oriented graphs, and becomes more tractable.
We determine the inducibility of all tournaments with at most $4$ vertices together with the extremal constructions. The $4$-vertex tournament containing an oriented $C_3$ and one source vertex has a particularly interesting extremal construction. It
In 1971, Tutte wrote in an article that it is tempting to conjecture that every 3-connected bipartite cubic graph is hamiltonian. Motivated by this remark, Horton constructed a counterexample on 96 vertices. In a sequence of articles by different aut
The random reversal graph offers new perspectives, allowing to study the connectivity of genomes as well as their most likely distance as a function of the reversal rate. Our main result shows that the structure of the random reversal graph changes d