Transfer Learning for Degree-Corrected Mixed Membership Network Models

TL;DR

This paper introduces a transfer learning method for the Degree-Corrected Mixed-Membership (DCMM) network model, improving estimation accuracy by leveraging source datasets and addressing computational and negative transfer issues.

Contribution

It develops a transfer learning procedure for DCMM models, with theoretical guarantees, a computationally efficient step, and an algorithm to select useful sources.

Findings

Transfer learning significantly improves estimation accuracy.

Eigenvalue gap explains the benefit of knowledge transfer.

The method performs well on real-world network datasets.

Abstract

Statistical analysis of network data has attracted considerable attention in recent years, due to the rapid advancement of well-trained network models and the accessibility of large public network datasets. In this article, we propose a transfer learning procedure for boosting estimation accuracy of a target network structure based on the well-known Degree-Corrected Mixed-Membership (DCMM) model in the literature. By leveraging useful information from informative source datasets, we theoretically prove that the transfer learning procedure greatly improve the estimation accuracy for the target connection probability matrix. Our theoretical analysis also reveals that the benefits from knowledge transfer in this context attributes to the enlarged eigenvalue gap of the target connection probability matrix. Additionally, we propose a random projection step in conjunction with the conventional aggregation procedure to alleviate the heavy computational burden in practice. In the presence of potentially harmful sources, we further provide an iterative truncation algorithm for selecting useful datasets and avoiding negative transfer. Numerical results showcase the practical utility of our methods in real-world network dataset analysis, including journal citation network dataset and international trade network dataset.