ProofComplete

Proof of Property B Bound via Lovász Local Lemma

A hypergraph has Property B (is 2-colorable) if its vertices can be colored with two colors such that no edge is monochromatic. We prove a sharp sufficient condition using the Lovász Local Lemma.


Statement

Theorem7.5Property B via LLL

Let H=(V,E)\mathcal{H} = (V, \mathcal{E}) be a kk-uniform hypergraph where every edge intersects at most dd other edges. If d≤2k−1/e−1d \leq 2^{k-1}/e - 1, then H\mathcal{H} has Property B.


Proof

Proof

Color each vertex independently and uniformly at random with red or blue. For each edge e∈Ee \in \mathcal{E}, let AeA_e be the bad event that ee is monochromatic. Then: Pr⁡[Ae]=2⋅(12)k=21−k.\Pr[A_e] = 2 \cdot \left(\frac{1}{2}\right)^k = 2^{1-k}.

Dependency graph: Two events AeA_e and AfA_f are dependent only if e∩f≠∅e \cap f \neq \emptyset. Since each edge intersects at most dd other edges, the dependency graph has maximum degree dd.

Applying the symmetric LLL: We need e⋅Pr⁡[Ae]⋅(d+1)≤1e \cdot \Pr[A_e] \cdot (d + 1) \leq 1, i.e., e⋅21−k⋅(d+1)≤1.e \cdot 2^{1-k} \cdot (d + 1) \leq 1.

This is equivalent to d+1≤2k−1/ed + 1 \leq 2^{k-1}/e, which holds by assumption.

By the Lovász Local Lemma: Pr⁡[⋂e∈EAe‾]>0.\Pr\left[\bigcap_{e \in \mathcal{E}} \overline{A_e}\right] > 0.

Therefore a proper 2-coloring exists, and H\mathcal{H} has Property B. □\square

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Discussion and Improvements

ExampleTightness of the Bound

The bound d≤2k−1/ed \leq 2^{k-1}/e is essentially tight. There exist kk-uniform hypergraphs with d=Θ(2k)d = \Theta(2^k) that are not 2-colorable. For instance, Erdos showed using the probabilistic method that random kk-uniform hypergraphs on nn vertices with Θ(n⋅2k)\Theta(n \cdot 2^k) edges are not 2-colorable with high probability.

RemarkConstructive Version

The Moser-Tardos algorithm makes this proof constructive: given the hypergraph satisfying the condition, one can efficiently find a proper 2-coloring. The algorithm randomly colors vertices, then iteratively resamples the colors of vertices in monochromatic edges. The expected number of resampling steps is at most ∣E∣/(1−e⋅21−k(d+1))=O(∣E∣)|\mathcal{E}| / (1 - e \cdot 2^{1-k}(d+1)) = O(|\mathcal{E}|).

ExampleIndependent Transversals

A related application: Let GG be a graph with vertex partition V1∪⋯∪VnV_1 \cup \cdots \cup V_n where ∣Vi∣≥2ed+1|V_i| \geq 2ed + 1 and each vertex has at most dd neighbors in each other part. Then GG has an independent transversal (a set choosing one vertex from each part with no edges between chosen vertices). This follows from the LLL with events AeA_{e} for each edge ee between parts.

RemarkEntropy Compression

The entropy compression method (Esperet-Parreau 2013) can sometimes beat the LLL bound. By analyzing the information content of the algorithm's execution, one obtains bounds of the form d≤2k−1(1−o(1))d \leq 2^{k-1}(1 - o(1)), removing the factor of ee in the denominator for large kk.