In real life, the actual topology of a network is often difficult to observe or even unobservable, which seriously limits our analysis and understanding of such networks. How to accurately infer the network structure from easily observed data is extremely urgent. In this letter, we try to improve the inference accuracy by introducing the heterogeneity of nodes during the network reconstruction, and propose a novel method to estimate the importance of nodes directly from the spreading results. The results on both synthetic and empirical data sets show that our algorithms can effectively improve the inference accuracy, especially when the observed data is insufficient.

在现实生活中，网络的实际拓扑结构往往难以观察甚至无法观察，这严重限制了我们对此类网络的分析和理解。如何从容易观察到的数据中准确推断出网络结构是非常紧迫的。在这封信中，我们尝试通过在网络重建过程中引入节点的异构性来提高推理精度，并提出了一种直接从传播结果估计节点重要性的新方法。在合成数据集和经验数据集上的结果表明，我们的算法可以有效地提高推理精度，尤其是在观测数据不足的情况下。