Embeddings of Nation-Level Social Networks

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 © 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.