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.

翻译：如今，荷兰、丹麦等国家浮现出完整的国家级社交网络，但这些网络在处理大规模、多模态、时变网络时带来了技术挑战。我们报告了在生成荷兰人口网络的动态节点嵌入方面的实践经验。我们提出了：（a）一种层感知随机游走策略，改进了多模态网络的传统平面化方法；（b）一种时间对齐策略，能将年度网络映射到同一嵌入空间，同时避免向未来年份泄露信息；以及（c）运用斐波那契螺旋和嵌入白化技术实现更均衡、高效的分割。我们证明了这些技术在构建基于嵌入的13个下游任务模型中的有效性。