## Lecture 1. Introduction

There are many interesting models of random graphs, and there a lot of different questions that can be asked about them. In this seminar we will only have the time cover a few of the most classical results. The goal of this first lecture is to give an informal overview of what we intend to cover this semester.

** What is a random graph? **

Let be a graph, where is the set of vertices and is the set of edges (that is, a set of pairs of vertices). We will consider only simple graphs, that is, graphs with undirected edges and with no self-edges. The graph structure is compactly described by the incidence matrix , where . Thus and for each .

Large, complex graphs and networks appear everywhere: the internet, social networks, etc.; and are often formed by some random process. One natural way to construct large complex graphs with interesting structure is to choose the incidence matrix randomly, which yields *random graphs*. Such models exhibit many fascinating probabilistic phenomena.

** Erdös-Rényi graphs **

Let be a random graph on vertices, such that is a collection of i.i.d. Bernoulli random variables with , . Such a random graph is called *Erdös-Rényi graph*, and we denote this model by .

The Erdös-Rényi graph is the simplest random graph model: for example, in a group of people, each pair are friends, independently, with probability . Unfortunately, this is not a realistic model of most real-world networks. On the other hand, such models are used in engineering applications (for example in coding theory), and already the theory of this simple model is very rich and should be understood before more complicated models can be tackled. This model was the first systematically studied, albeit a slightly different form, by Erdös and Rényi in the late 1950s. More complicated models capture certain features of real-world graphs that are not well described by the Erdös-Rényi model, such as the small world and preferential attachment models; see the book by Durrett or the lecture notes by van der Hofstad, for example. Unfortunately, we will not have time to cover such models in this seminar.

The general question we will aim to address is: “What does a *large* Erdös-Rényi graph look like?” This question is rather vague. It turns out that there are two basic regimes in which different questions are of interest. In order to describe these two regimes, let us define the “complexity” of a graph as

This definition is really intended for connected graphs, where it is a meaningful quantity.

For an Erdös-Rényi random graph we have

since . Of course, we do not know at this point whether the Erdös-Rényi graph is connected (this will be discussed below), but this formula can serve as an informal guide to motivate the two regimes of interest.

**Fixed edge probability**. Fix and let go to infinity. We expect such a graph to be very dense: on average, a fraction of all possible edges is present, and each vertex is connected with other vertices. In this regime the expected complexity of the graph blows up, that is, . So, we have to choose questions to study that are interesting for a dense graph where the structure is rich: we need a “rich” way of measuring properties in this regime.**Low complexity**. Take for some and let go to infinity. This is the largest edge probability that we can take so that the expected complexity of the graph does not blow up, since the number of edges and the number of vertices are of the same order. In this regime we get different behavior of the graphs depending on the value of .

Clearly, the random graphs in regimes 1 and 2 will look quite different.

**Regime 1: Fixed edge probability**

In this regime the graph is very rich. What questions are meaningful? Heuristically, a rich graph must contain interesting subgraphs, and must possess a complex combinatorial structure. We now state two results in this spirit that we are going to prove in the following lectures. The proofs of these results are nice examples of use of the *probabilistic method*.

**Clique number**. A *clique* in a graph is a complete subgraph, that is, a subset such that for every , . If describes friendships, a clique is a group of mutual friends. Intuitively, a rich graph must contain a large clique. The *clique number* is the cardinality of the largest clique in .

Theorem 1Let be an Erdös-Rényi graph. Then

On a heuristic level, we see that this result makes sense since

from which we see that is the critical case. Of course, one could also study similar questions for other types of subgraphs, but we will not do that.

**Chromatic number**. A coloring of is a color assignment to each vertex in such that for each , and have a different color. The *chromatic number* is the smallest number of colors needed to color . It is clear that the chromatic number of a graph tells us something about the complexity of the graph.

Theorem 2Let be an Erdös-Rényi graph. Then

So, in the Erdös-Rényi graph the chromatic number grows as , smaller than in a complete graph were the chromatic number is , but much larger than in trees which can be colored with only 2 colors regardless of their size. That the same factor appears in both theorems above is not a coincidence: there is a relation between clique number and chromatic number that forms the basis for the proof.

**Regime 2: Low complexity**

In this regime we consider the case , so each vertex has neighbors on average:

In this case we expect the graph to be much simpler than in the previous regime. If is small, for example, we expect the graph to be very disconnected, with a lot of small connected pieces. It turns out that this can be made precise. The following is a somewhat informal statement of a theorem that we will prove in detail in the following lectures.

“Theorem” 3For , let be the connected component of that contains .

- If , then .
- If , then and all other components have size .
- If , then and there are several large components.
Moreover, all with are either trees or have one cycle with high probability.

So, decomposes into many disconnected, simple pieces in the *subcritical case* . In the *supercritical case* we get emergence of one *giant component* that is complex, with some remaining disconnected simple pieces. The *critical case* is the most complicated. In this case there are additional phase transitions near the *critical window* , with non trivial limit distribution for the component sizes and complexities. [Note that in all these cases the Erdös-Rényi graph will have multiple connected components; the threshold for the entire graph to be connected turns out to be .]

How do we prove such a theorem? We will use a dynamical method: start at , then explore step by step much as in a game of Minesweeper. We then analyze the hitting times of this “random walk”. One can also study Brownian scaling limits of such random walks, which provides a mechanism to define “infinite limiting objects” in this theory.

*Many thanks to Patrick Rebeschini for scribing this lecture!*