Embeddings of Nation-Level Social Networks
This work addresses the challenge of embedding large, multiplex, time-dependent social networks for researchers working with nation-scale data.
The authors developed techniques for dynamic node embeddings of nation-scale social networks, including a layer-sensitive random walk, temporal alignment, and embedding whitening, achieving improved performance on 13 downstream tasks.
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.