At , explore the directed out-cluster of the origin by a deterministic breadth-first procedure. Maintain the set of reached graph vertices and a queue of untested edges with one reached and one unreached endpoint. For each such edge, reveal its orientation. Add the unreached endpoint if the arrow points from the reached endpoint to it; otherwise discard that edge. edges whose endpoints are both already reached need no further test.
An untested edge has an independent fair orientation even though the choice of the next edge depends on earlier discoveries. Its probability of pointing toward the unreached endpoint is , whichever geometric direction that represents. Thus the discovery procedure has exactly the same transition probabilities as the ordinary bond percolation cluster exploration at density . In particular, for every , the probability of reaching at least graph vertices agrees in the two models. The uniform random orientation out-cluster comparison implies
A locally finite graph has an infinite directed simple ray from the origin exactly when its directed out-cluster is infinite: the exploration predecessor edges form a finitely branching rooted tree, to which the König infinity lemma applies. Conversely, such a ray visits infinitely many reachable graph vertices. Hence
Fairness of orientations is essential to this comparison; for general the success probability depends on the geometric direction of exploration. As usual, an infinite directed path means a ray with distinct graph vertices. Allowing a finite directed cycle to be traversed repeatedly would define an infinite walk instead and would make the claimed conclusion false.
The Burton-Keane theorem says that for independent bond percolation on the hypercubic lattice , , at every fixed , there is almost surely at most one infinite percolation cluster. It does not assert that such a cluster exists, nor does it assert absence of percolation at the critical parameter. The cases and are immediate; assume for the proof.
Let be the number of infinite percolation clusters. This random variable is translation invariant. By translation ergodicity of Bernoulli percolation, it is almost surely a constant . One way to see ergodicity is to approximate an invariant event by events on finite sets of edges, translate one approximation far enough to make the two approximations independent, and then let the approximation error vanish. Its probability equals its square, hence is zero or one.
The other probabilistic input is the finite-energy property of Bernoulli percolation. Conditionally on all edges outside a finite set, every prescribed inside configuration has positive probability, namely a product of powers of and . Thus an event of positive probability can be followed by a specified finite modification and still gives a positive-probability image event. Outside infinite arms are unaffected.
First exclude a finite . Some finite box meets at least two infinite percolation clusters with positive probability. Open all edges in and incident to that box. These clusters merge. Opening finitely many edges cannot create an infinite percolation cluster solely out of finite clusters, since it joins only finitely many such clusters. Hence the number of infinite percolation clusters decreases with positive probability, contradicting its almost-sure constant value. This rules out every finite multiplicity greater than one.
Now suppose . With positive probability a finite box meets at least three distinct infinite percolation clusters. Retain an exterior infinite ray from each of three of them. Replace the finitely many interior and boundary edges by a minimal tree joining their three attachment graph vertices, and close all unused edges there. The exterior rays belong to distinct original clusters, so have no outside connections to each other. The resulting joined cluster has a branch graph vertex with exactly three incident open edges, and deleting leaves three infinite components. If a terminal lies in the middle of the interior connecting path, its exterior ray supplies the third branch there. Such a graph vertex is a trifurcation vertex in percolation. A finite box has only finitely many candidate locations and modifications; therefore some fixed graph vertex has positive trifurcation probability. By translation invariance, that probability is a common number at every graph vertex.
The decisive deterministic input is the trifurcation boundary-counting lemma. Let be all these degree-three trifurcation vertices in percolation in a finite box . In each infinite percolation cluster meeting , delete the graph vertices of , contract each remaining connected component of a graph adjacent to to a node, and retain the incidences with the deleted graph vertices, including direct edges between graph vertices of . This is a finite tree: a cycle would connect two neighbours of an original trifurcation vertex in percolation without passing through it, contradicting its three-way separation. Every deleted graph vertex has degree three. Every leaf component is infinite, since a finite leaf would give a finite branch after removing its adjacent trifurcation vertex in percolation. The elementary tree degree identity therefore supplies at least infinite leaf components for a cluster containing .
Each such infinite component reaches the outer graph vertex boundary , and different components reach different boundary graph vertices. Summing over the clusters yields, in particular,
For a nearest-neighbour lattice box,
Taking expectations and using the common trifurcation probability gives
This contradicts and excludes infinitely many infinite percolation clusters. Thus only or remains:
The proof explains the role of the amenable boundary growth of lattice boxes: finite-energy property of Bernoulli percolation would make branching points have positive volume density, while their disjoint infinite branches can only be supplied through a boundary of lower order. It uses no planar duality and works in every indicated dimension.
Put and . The hypothesis implies . For the number of isolated vertices, the isolated vertices in the Erdős-Rényi model formulas give
Thus the Chebyshev inequality gives with high probability, uniformly over the specified range.
The Cayley formula supplies a spanning tree on any connected vertex set. Consequently the expected value of the number of graph components of order is bounded by
Choose a fixed with . For , with , the binomial coefficient bound gives
Here . The geometric series and a union bound therefore exclude this entire size range, since . For , an empty graph cut would be necessary. Its total probability is at most
There are consequently no graph components of orders between and , with high probability.
For each fixed , a graph component containing a graph cycle has a spanning tree and at least one extra edge. Counting that extra edge gives an expected value ; hence every small graph component is a tree component. The total number of vertices in tree components of orders satisfies
The Markov inequality gives with high probability. When , this sum is empty and . Since , the remaining vertices must lie in one graph component larger than . It is unique, and its order is . Every other graph component is a tree with at most vertices. This is the logarithmic-regime giant component mechanism: almost all missing vertices are isolated.