Predictive Subsampling for Scalable Inference in Networks

TL;DR

This paper introduces a subsampling method for scalable statistical inference in large networks, enabling efficient estimation and hypothesis testing by analyzing smaller subgraphs and interpolating results.

Contribution

The authors propose a novel subsampling approach within the generalized random dot product graph framework, providing theoretical guarantees and practical validation for scalable network inference.

Findings

Method reduces computational complexity for large networks

Consistency guarantees established for the subsampling approach

Simulation studies confirm practical effectiveness

Abstract

Network datasets appear across a wide range of scientific fields, including biology, physics, and the social sciences. To enable data-driven discoveries from these networks, statistical inference techniques like estimation and hypothesis testing are crucial. However, the size of modern networks often exceeds the storage and computational capacities of existing methods, making timely, statistically rigorous inference difficult. In this work, we introduce a subsampling-based approach aimed at reducing the computational burden associated with estimation and two-sample hypothesis testing. Our strategy involves selecting a small random subset of nodes from the network, conducting inference on the resulting subgraph, and then using interpolation based on the observed connections between the subsample and the rest of the nodes to estimate the entire graph. We develop the methodology under the generalized random dot product graph framework, which affords broad applicability and permits rigorous analysis. Within this setting, we establish consistency guarantees and corroborate the practical effectiveness of the approach through comprehensive simulation studies.