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The friendship paradox for sparse random graphs

Rajat Subhra Hazra, Frank den Hollander, Azadeh Parvaneh

Abstract

Let $G_n$ be an undirected finite graph on $n\in\mathbb{N}$ vertices labelled by $[n] = \{1,\ldots,n\}$. For $i \in [n]$, let $Δ_{i,n}$ be the friendship bias of vertex $i$, defined as the difference between the average degree of the neighbours of vertex $i$ and the degree of vertex $i$ itself when $i$ is not isolated, and zero when $i$ is isolated. Let $μ_n$ denote the friendship-bias empirical distribution, i.e., the measure that puts mass $\frac{1}{n}$ at each $Δ_{i,n}$, $i \in [n]$. The friendship paradox says that $\int_{\mathbb{R}} xμ_n(\mathrm{d}x) \geq 0$, with equality if and only if in each connected component of $G_n$ all the degrees are the same. We show that if $(G_n)_{n\in\mathbb{N}}$ is a sequence of sparse random graphs that converges to a rooted random tree in the sense of convergence locally in probability, then $μ_n$ converges weakly to a limiting measure $μ$ that is expressible in terms of the law of the rooted random tree. We study $μ$ for four classes of sparse random graphs: the homogeneous Erdős-Rényi random graph, the inhomogeneous Erdős-Rényi random graph, the configuration model and the preferential attachment model. In particular, we compute the first two moments of $μ$, identify the right tail of $μ$, and argue that $μ([0,\infty))\geq\tfrac{1}{2}$, a property we refer to as friendship paradox significance.

The friendship paradox for sparse random graphs

Abstract

Let be an undirected finite graph on vertices labelled by . For , let be the friendship bias of vertex , defined as the difference between the average degree of the neighbours of vertex and the degree of vertex itself when is not isolated, and zero when is isolated. Let denote the friendship-bias empirical distribution, i.e., the measure that puts mass at each , . The friendship paradox says that , with equality if and only if in each connected component of all the degrees are the same. We show that if is a sequence of sparse random graphs that converges to a rooted random tree in the sense of convergence locally in probability, then converges weakly to a limiting measure that is expressible in terms of the law of the rooted random tree. We study for four classes of sparse random graphs: the homogeneous Erdős-Rényi random graph, the inhomogeneous Erdős-Rényi random graph, the configuration model and the preferential attachment model. In particular, we compute the first two moments of , identify the right tail of , and argue that , a property we refer to as friendship paradox significance.
Paper Structure (42 sections, 14 theorems, 211 equations, 4 figures)

This paper contains 42 sections, 14 theorems, 211 equations, 4 figures.

Key Result

Theorem 2.2

If $(G_{n})_{n\in\mathbb N}$ converges locally in probability to the almost surely locally finite, connected rooted random graph $(G,o)$, then $\mu_{n} \Longrightarrow \mu$ as $n\to\infty$ in probability. In particular, as $n\to\infty$,

Figures (4)

  • Figure 1: Numerical plot of the map $\lambda\mapsto \sum_{k=0}^{10^{4}} \tfrac{\mathrm{e}^{-\lambda}\lambda^{k}}{k!} \sum_{l\geq k(k-1)} \tfrac{\mathrm{e}^{-\lambda k}(\lambda k)^{l}}{l!}$ for $10^{-8} \leq \lambda \leq 10^3$ for $\mathrm{HER}_{n}(\frac{\lambda}{n} \wedge 1)$.
  • Figure 2: Numerical plot of the map $\lambda\mapsto \mu([0,\infty))$ for $\mathrm{IER}_{n}(\lambda f)$ with an increasing, a decreasing and a non-monotonic function $f$, estimated with the help of Monte Carlo integration.
  • Figure 3: Numerical plot of the region containing the map $\tau\mapsto \mu([0,\infty))$ for $\mathrm{CM}_{n}(\mathbf{d}_{n})$ with $p_{k} = k^{-\tau}/\zeta(\tau)$.
  • Figure 4: Numerical plot of a lower bound on $\delta\mapsto \mu([0,\infty))$ for $(\mathrm{PAM}_{n}^{(1,\delta)})_{n\in\mathbb N}$.

Theorems & Definitions (22)

  • Definition 2.1
  • Theorem 2.2
  • Theorem 2.3
  • Remark 2.4
  • Definition 2.5
  • Conjecture 2.6
  • proof : Proof of Theorem \ref{['thm1']}
  • proof : Proof of Theorem \ref{['thm2']}
  • Theorem 4.1
  • Theorem 4.2
  • ...and 12 more