A fundamental problem in the study of networks is the identification of important nodes. This is typically achieved using centrality metrics, which rank nodes in terms of their position in the network. This approach works well for static networks, that do not change over time, but does not consider the dynamics of the network. Here we propose instead to measure the importance of a node based on how much a change to its strength will impact the global structure of the network, which we measure in terms of the spectrum of its adjacency matrix. We apply our method to the identification of important nodes in equity transaction networks, and we show that, while it can still be computed from a static network, our measure is a good predictor of nodes subsequently transacting. This implies that static representations of temporal networks can contain information about their dynamics.

翻译：网络研究中的一个基本问题是确定重要的节点。这通常是使用中心点指标实现的,按其在网络中的位置排列节点。这种方法对静态网络运作良好,不会随着时间变化而变化,但并不考虑网络的动态。我们在这里建议衡量一个节点的重要性,其依据是其强度的变化将在多大程度上影响网络的全球结构,我们用其相邻矩阵的频谱来衡量这一结构。我们用我们的方法来确定股权交易网络中的重要节点,我们表明,虽然它仍然可以从静态网络中计算,但我们的计量是随后节点转换的良好预测。 这意味着时间网络的静态显示可以包含有关其动态的信息。