DyANE：时态网络的动态感知节点嵌入
DyANE: Dynamics-aware node embedding for temporal networks
摘要：
网络节点的低维向量表示已被证明可以成功地将图形数据提供给机器学习算法，并提高不同任务的性能。然而，大多数嵌入技术都是为了实现网络结构和模式的密集、低维编码而开发的。在这里，我们提出了一种节点嵌入技术，旨在提供低维特征向量，这些特征向量提供时间网络上发生的动态过程的信息，而不是网络结构本身的信息，目标是实现与这些过程的演变和结果相关的预测任务。我们实现这一点，通过使用修改后的超邻接表示的时间网络和标准的嵌入技术的基础上的静态图的随机行走。我们表明，由此产生的嵌入向量是有用的预测任务有关的范式动态过程，即传染病传播经验的时间网络。特别是，我们说明了我们的方法预测节点的流行病状态在一个单一的传播过程的情况下的性能。我们展示了如何将此任务框定为嵌入向量上的监督多标签分类任务，使我们能够从随机时间的部分节点采样中估计整个系统的时间演变，并对即时预报传染病动态产生潜在影响。
英文摘要：
Low-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks -- rather than of the network structure itself -- with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random-walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes' epidemic states in a single instance of a spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
