Densities of cliques and independent sets in graphs
- Slides: 31
Densities of cliques and independent sets in graphs Yuval Peled, HUJI Joint work with Nati Linial, Benny Sudakov, Hao Huang and Humberto Naves.
High level motivation § § How can we study large graphs? Approach: Sample small sets of vertices and examine the induced subgraphs. § § What graph properties can be inferred from its local profile? What are the possible local profiles of large graphs?
Local profiles of graphs § § What are the possible local profiles of (large) graphs? For graphs H, G, we denote by d(H; G) the induced density of H in G, i. e. d(H; G): = The probability that |H| random vertices in G induce a copy of H.
Local profiles of graphs Definition: Given a family of graphs, is the set of all such that , a sequence of graphs with § and § Problem: Characterize this set.
Local profiles of graphs § § Characterizing seems to be a hard task: A mathematical perspective: § Many hard problems fall into this framework. § E. g. for t=1, the problem is equivalent to computing the inducibility of graph, a parameter known only for a handful of graphs. § A computational perspective: § [Hatami, Norine 11’]: Satisfiability of linear inequalities in is undecidable.
Local profiles of two cliques § The case of two cliques is already of interest: Turan’s Theorem: § Kruskal-Katona Theorem: (r<s) § § Minimize subject to this constraint? much harder: solved only recently for r=2: Razborov 08’ (s=3), Nikiforov 11’ (s=4), Reiher (arbitrary s)
A clique and an anticlique § Motivation - quantitative versions of Ramsey’s theorem: § Investigate distributions of monochromatic cliques in a red/blue coloring of the complete graph. § § Goodman’s inequality: The minimum is attained by G(n, ½), conjectured by Erdos to minimize for every r. § Refuted by Thomasson for every r>3.
A clique and an anticlique (II) § § A consequence from Goodman’s inequality: [Franek-Rodl 93’] The analog of this is false for r=4, by a blow up of the following graph: § V = {0, 1}^13, v~u iff dist(v, u) ∈ {1, 4, 5, 8, 9, 11} § § Fundamental open problem: Find graphs with few cliques and anticliques. We are interested in the other side of
Many cliques and anticliques How big can both d(Ks; G) and d(Kr; G) be?
Many cliques and anticliques What graphs has many cliques and anticliques? § First guess: A clique on some fraction of the vertices Example: r=s=3. 1 0. 9 0. 8 t 1 -t 0. 7 0. 6 0. 5 § Second guess: Complements of these graphs 0. 4 0. 3 0. 2 0. 1 0 0 0. 2 0. 4 0. 6 0. 8 1
Main theorem Let r, s > 2. Suppose that and let q be the unique root in [0, 1] of Then, Namely, given the maximum of is attained in one of two graphs: a clique on a fraction of the vertices, or the complement of such graph.
More theorems § Stability: such that every sufficiently large graph G with is close to the extremal graph. § Max-min: where
Proof of main theorem Strategy: I. Reduce the problem to threshold graphs. II. Reformulate the problem for threshold graphs as an optimization problem. III. Characterize the solutions of the optimization problem. §
Proof of main theorem Strategy: I. Reduce the problem to threshold graphs. II. Reformulate the problem for threshold graphs as an optimization problem. III. Characterize the solutions of the optimization problem. §
Shifting in a nutshell § Given a graph G and vertices u, v the shift of G from u to v is defined by the rule: § Every other vertex w with w~u and w≁v gets disconnected from u and connected to v. § § A graph G with V=[n] is said to be shifted if for every i<j the shift of G from j to i does not change G. Fact: Every graph can be made shifted by a finite number of shifting operations.
Shifting cliques § § § Lemma: Shifting does not decrease the number of s-cliques in the graph. Proof: Consider the shift from j to i. If a subset C of V forms a clique in G and not in the shifted graph S(G), then C {j} U {i} forms a clique in S(G) and not in G. Cor: By symmetry, shifting does not decrease the number of r-anticliques.
Threshold graphs § Def: A graph is called a threshold graph if there is an order on the vertices, such that every vertex is adjacent to either all or none of its predecessors. § Lemma: A shifted graph is a threshold graph. Proof: Consider the following order: § Cor: The extremal graph is a threshold graph. §
Proof of main theorem Strategy: I. Reduce the problem to threshold graphs. II. Reformulate the problem for threshold graphs as an optimization problem. III. Characterize the solutions of the optimization problem. §
Threshold graphs § Every threshold graph G can be encoded as a point in A_1 A_2 A_3 A_4 A_2 k-1 A_2 k
densities in threshold graphs § The densities are (upto o(1)): A_1 A_2 A_3 A_4 A_2 k-1 A_2 k
Optimization problem § § The new form of our optimization problem is: We need to prove that every maximum is either supported on x_1, y_1 or on y_1, x_2.
Optimization problem § It suffices to show that for every a, b>0, the maximum of is either supported on x_1, y_1 or on y_1, x_2. Why? For both problems have the same set of maximum points.
Proof of main theorem Strategy: I. Reduce the problem to threshold graphs. II. Reformulate the problem for threshold graphs as an optimization problem. III. Characterize the solutions of the optimization problem. §
Technical lemma § Let k, r, s≥ 2 be integers, a, b>0 reals, and the polynomials defined above. Then, every non-degenerate maximum of is either supported on x_1, y_1 or on y_1, x_2. (x, y) is non-degenerate if the zeros in the sequence (y_1, x_2, y_3, …, x_k, y_k) form A_1 A_3 A_2 A_4 a suffix. A_1 U A_3 A_2 k-1 A_2 k
Proof § Let (x, y) be a non-degenerate maximum of f: § , otherwise we can increase f by a perturbation that increases the smaller element. § WLOG x_1>0, otherwise x exchange roles with y, and p with q (by looking at the complement graph). § We show that x_3=y_2=x_2=0.
Proof (x_3=0) § § § Define the following matrices: If x_3>0 and (x, y) is non-degenerate then B is positive definite. For , let x’ be defined by
Proof (x_3=0) (II) § § § Then, If A is singular – choose Av=0, v≠ 0. If A is invertible – choose
Proof (x_3=0) (III) § Hence, contradicting the maximality of f(x, y). § Proving y_2=0, x_2=0 is done with similar methods.
Remarks § For the max-min theorem: Consider 1 0. 9 (a=b=1). 0. 8 0. 7 § For r=s=3, Goodman inequality and our bound completely determine the set 0. 6 0. 5 0. 4 0. 3 § Stability – obtained using Keevash’s stable Kruskal-Katona theorem. 0. 2 0. 1 0 0 0. 2 0. 4 0. 6 0. 8 1
The End ?
§ For l≤m, § Hence, and
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