Optimal estimators and tests for reciprocal effects

TL;DR

This paper introduces a novel cycle count ratio method to efficiently estimate the reciprocal effect parameter in the $p_1$ model for directed networks, achieving optimal rates and phase transition in diverse settings.

Contribution

It discovers a pair of generalized cycles whose count ratio simplifies to exp(ρ), enabling new estimators and tests for the reciprocal effect in complex network models.

Findings

Estimator achieves optimal rate in diverse network settings.

Test achieves optimal phase transition.

Method works without strong assumptions on network sparsity or heterogeneity.

Abstract

The $p_1$ model plays a fundamental role in modeling directed networks, where the reciprocal effect parameter $\rho$ is of special interest in practice. However, due to nonlinear factors in this model, how to estimate $\rho$ efficiently is a long-standing open problem. We tackle the problem by the cycle count approach. The challenge is, due to the nonlinear factors in the model, for any given type of generalized cycles, the expected count is a complicated function of many parameters in the model, so it is unclear how to use cycle counts to estimate $\rho$. However, somewhat surprisingly, we discover that, among many types of generalized cycles with the same length, we can carefully pick a pair of them such that in the ratio between the expected cycle counts of the two types, the non-linear factors cancel out nicely with each other, and as a result, the ratio equals to $\mathrm{exp}(\rho)$ exactly. Therefore, though the expected count of cycles of any type is not tractable, the ratio between the expected cycle counts of a (carefully chosen) pair of generalized cycles may have an utterly simple form. We study to what extent such pairs exist, and use our discovery to derive both an estimate for $\rho$ and a testing procedure for testing $\rho = \rho_0$. In a setting where we allow a wide range of reciprocal effects and a wide variety of network sparsity and degree heterogeneity, we show that our estimator achieves the optimal rate and our test achieves the optimal phase transition. Technically, first, motivated by what we observe on real networks, we do not want to impose strong conditions on reciprocal effects, network sparsity, and degree heterogeneity. Second, our proposed statistic is a type of $U$-statistic, the analysis of which involves complex combinatorics and is error-prone. For these reasons, our analysis is long and delicate.