### On replica symmetry of large deviations in random graphs

Eyal Lubetzky and I just uploaded to the arXiv our new paper “On replica symmetry of large deviations in random graphs.” In this paper we answer the following question of Chatterjee and Varadhan:

Question. Fix ${0. Let ${n}$ be a large integer and let ${G}$ be an instance of the Erdős-Rényi random graph ${\mathcal G(n,p)}$ conditioned on the rare event that ${G}$ has at least as many triangles as the typical ${\mathcal G(n,r)}$. Does ${G}$ look like a typical ${\mathcal G(n,r)}$?

Here the Erdős-Rényi random graph ${\mathcal G(n,p)}$ is formed by taking ${n}$ vertices and adding every possible edge independently with probability ${p}$. Here “look like” means close in cut-distance (but we won’t give a precise definition in this blog post). In this case, saying that ${G}$ is close to ${\mathcal G(n,r)}$ is roughly equivalent to saying that every not-too-small subset of vertices (at least constant fraction in size) of ${G}$ induce a subgraph with edge density close to ${r}$.

Another way to phrase the question is: what is the reason for ${G}$ having too many triangles? Is it because it has an overwhelming number of edges uniformly distributed, or some fewer edges arranged in a special structure, e.g., a clique.

Via a beautiful new framework by Chatterjee and Varadhan for large deviation principles in ${\mathcal G(n,p)}$, we give a complete answer to the above question.

The answer, as it turns out, depends on ${(p,r)}$. See the plot below. For ${(p,r)}$ in the blue region, the answer is yes, and for ${(p,r)}$ in the red region, the answer is no.

Does ${G}$ looks like a ${\mathcal G(n,r)}$?

The phase transition behavior has already been observed previously by Chatterjee and Varadhan, but the determination of the exact precise phase boundary is new. Borrowing language from statistical physics, the blue region where the conditional random graph is close to ${\mathcal G(n,r)}$ is called the replica symmetric phase, and the red region is called the symmetry breaking phase. Note that in the left part of the plot, as we fix a small value of ${p}$, the model experiences a double phase transition as ${r}$ increases from ${p}$ to ${1}$ — starting first in the blue phase, then switches to the red phase, and then switches back to the blue phase.

More generally, our result works for any ${d}$-regular graph in replace of triangles. The boundary curve depends only on ${d}$, and they are plotted below for the first few values of ${d}$. In particular, this means that large deviation for triangles and 4-cycles share the same phase boundary. A pretty surprising fact! We also consider the model where we condition on the largest eigenvalue of the graph being too large, and the phase boundary also turns out to be the same as that of triangles.

We also derive similar results for large deviations in the number of linear hypergraphs in a random hypergraph.

The phase boundary for ${d}$-regular graphs

### Exponential random graphs

We also studied the exponential random graph model. This is a widely studied graph model, motivated in part by applications in social networks. The idea is to bias the distribution of the random graph to favor those with, say, more triangles. This model has a similar flavor to the model considered above where we condition on the random graph having lots of triangles.

We consider a random graph ${G}$ on ${n}$ vertices drawn from the distribution

$\displaystyle p_\beta(G) \propto \exp\left(\tbinom{n}{2}(\beta_1 t(K_2, G) + \beta_2 t(K_3, G))\right)\,,$

where ${t(K_2, G)}$ and ${t(K_3, G)}$ are the edge density and the triangle density of ${G}$, respectively. When ${\beta_2 = 0}$, this model coincides with the Erdős-Rényi model ${\mathcal G(n,p)}$ with some ${p}$ depending on ${\beta_1}$. We only consider the case ${\beta_2 > 0}$, which represents a positive bias in the triangle count.

As shown by Bhamidi, Bresler and Sly and Chatterjee and Diaconis, when ${n}$ is large, a typical random graph drawn from the distribution has a trivial structure — essentially the same one as an Erdős-Rényi random graph with a suitable edge density. This somewhat disappointing conclusion accounts for some of the practical difficulties with statistical parameter estimation for such models. In our work, we propose a natural generalization that will enable the exponential model to exhibit a nontrivial structure instead of the previously observed Erdős-Rényi behavior.

Here is our generalization. Consider the exponential random graph model which includes an additional exponent ${\alpha>0}$ in the triangle density term:

$\displaystyle p_{\alpha,\beta}(G) \propto \exp\left(\tbinom{n}{2}(\beta_1 t(K_2, G) + \beta_2 t(K_3, G)^\alpha)\right) \, .$

When ${\alpha \geq 2/3}$, the generalized model features the Erdős-Rényi behavior, similar to the previously observed case of ${\alpha = 1}$. However, for ${0< \alpha < 2/3}$, there exist regions of values of ${(\beta_1, \beta_2)}$ for which a typical random graph drawn from this distribution has symmetry breaking, which was rather unexpected given earlier results. For example, we know that there is symmetry breaking in the shaded regions in the plots below. (The blue curve indicates a discontinuity in the model which we won’t discuss in this blog post.)

Symmetry breaking in the new exponential graph model