Giant component

In network theory, a giant component is a connected component of a given random graph that contains a significant fraction of the entire graph's vertices.

More precisely, in graphs drawn randomly from a probability distribution over arbitrarily large graphs, a giant component is a connected component whose fraction of the overall number of vertices is bounded away from zero.

In sufficiently dense graphs distributed according to the Erdős–Rényi model, a giant component exists with high probability.

Giant components are a prominent feature of the Erdős–Rényi model (ER) of random graphs, in which each possible edge connecting pairs of a given set of n vertices is present, independently of the other edges, with probability p. In this model, if

goes to infinity) all connected components of the graph have size O(log n), and there is no giant component.

there is with high probability a single giant component, with all other components having size O(log n).

, intermediate between these two possibilities, the number of vertices in the largest component of the graph,

[1] Giant component is also important in percolation theory.

, is removed randomly from an ER network of degree

there exists a giant component (largest cluster) of size,

, the distribution of cluster sizes behaves as a power law,

which is a feature of phase transition.

Alternatively, if one adds randomly selected edges one at a time, starting with an empty graph, then it is not until approximately

More precisely, when t edges have been added, for values of t close to but larger than

, the size of the giant component is approximately

[1] However, according to the coupon collector's problem,

edges are needed in order to have high probability that the whole random graph is connected.

A similar sharp threshold between parameters that lead to graphs with all components small and parameters that lead to a giant component also occurs in tree-like random graphs with non-uniform degree distributions

The degree distribution does not define a graph uniquely.

However, under the assumption that in all respects other than their degree distribution, the graphs are treated as entirely random, many results on finite/infinite-component sizes are known.

In this model, the existence of the giant component depends only on the first two (mixed) moments of the degree distribution.

Let a randomly chosen vertex have degree

When there is no giant component, the expected size of the small component can also be determined by the first and second moments and it is

However, when there is a giant component, the size of the giant component is more tricky to evaluate.

[2] Similar expressions are also valid for directed graphs, in which case the degree distribution is two-dimensional.

[5] There are three types of connected components in directed graphs.

By definition, the average number of in- and out-edges coincides so that

is the generating function of the degree distribution

For directed networks, generating function assigned to the joint probability distribution

The criteria for giant component existence in directed and undirected random graphs are given in the table below:

An Erdős–Rényi–Gilbert random graph with 1000 vertices at the critical edge probability , showing a large component and many small ones. At this edge probability, the large component is not yet a giant component: it contains only a sublinear number of vertices.