Variability of Dynamic Node Embeddings

在过去的五年里，节点嵌入算法吸引了很多研究兴趣，神经方法可以说是最流行的方法之一。许多最先进的节点嵌入算法利用随机过程，即使在固定参数下也会产生同一图的多个不同嵌入。这些不稳定性自然会影响在节点嵌入之上执行的任何下游任务的结果。申请人最近进行的一项研究表明，这可能特别影响实例级下游分类。因此，当使用节点嵌入时，不稳定性效应会带来一个敏感的问题，在考虑动态演化图时更是如此。在这个项目中，我们的目标是深入理解动态演化图的节点嵌入，特别注意理解可变性。此外，我们预计探索有效的算法，能够嵌入动态图，同时控制所产生的嵌入的变化。总的来说，我们的工作有助于开发更有原则的网络表征学习方法。

Node embedding algorithms have attracted much research interest over the last five years, with neural approaches arguably being among the most popular approaches. Many of the state-of-the-art node embedding algorithms utilize random processes that yield multiple different embeddings of the same graph even under fixed parameters. These instabilities naturally influence the outcome of any downstream task that is performed on top of node embeddings. A recent study conducted by the applicants has shown that this can particularly affect instance-level down-stream classifications. As a result, instability effects pose a sensitive issue when working with node embeddings, even more so when considering dynamically evolving graphs. In this project, we aim to develop a deep understanding of node embeddings for dynamically evolving graphs, with particular attention paid to understanding variability. In addition, we anticipate exploring efficient algorithms that are capable of embedding dynamic graphs while controlling the variability of resulting embeddings. Overall, our work contributes to the development of more principled approaches for network representation learning.