Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time. We present attributed unfolded adjacency spectral embedding (AUASE), a stable unsupervised representation learning framework for dynamic networks in which nodes are attributed with time-varying covariate information. To establish stability, we prove uniform convergence to an associated latent position model. We quantify the benefits of our dynamic embedding by comparing with state-of-the-art network representation learning methods on three real attributed networks. To the best of our knowledge, AUASE is the only attributed dynamic embedding that satisfies stability guarantees without the need for ground truth labels, which we demonstrate provides significant improvements for link prediction and node classification.

翻译：动态网络嵌入的稳定性确保了在不同时间表现相同的节点获得相同的嵌入表示，从而允许跨时间比较网络中的节点。我们提出了属性展开邻接谱嵌入（AUASE），这是一种用于动态网络的稳定无监督表示学习框架，其中节点具有随时间变化的协变量信息。为建立稳定性，我们证明了其一致收敛于一个相关的潜在位置模型。通过在三个真实属性网络上与最先进的网络表示学习方法进行比较，我们量化了所提动态嵌入方法的优势。据我们所知，AUASE是唯一无需真实标签即可满足稳定性保证的属性动态嵌入方法，我们证明了该方法在链路预测和节点分类任务上带来了显著改进。