Embeddings of Nation-Level Social Networks

TL;DR

This paper introduces novel methods for creating dynamic, nation-scale social network embeddings, addressing challenges of size, multiplexity, and temporal alignment, demonstrated on the Netherlands network.

Contribution

It presents a layer-sensitive random walk, temporal alignment strategy, and embedding techniques for large, time-dependent multiplex networks.

Findings

Improved embedding quality over traditional methods

Effective alignment of annual networks in a common space

Successful application to 13 downstream tasks

Abstract

Full nation-scale social networks are now emerging from countries such as the Netherlands and Denmark, but these networks present challenging technical issues in working with large, multiplex, time-dependent networks. We report on our experiences in producing dynamic node embeddings of the population network of the Netherlands. We present (a) a layer-sensitive random walk strategy which improves on traditional flattening methods for multiplex networks, (b) a temporal alignment strategy that brings annual networks into the same embedding space, without leaking information to future years, and (c) the use of Fibonacci spirals and embedding whitening techniques for more balanced and effective partitioning. We demonstrate the effectiveness of these techniques in building embedding-based models for 13 downstream tasks.