论文标题
部分可观测时空混沌系统的无模型预测
Improving mean-field network percolation models with neighbourhood information
论文作者
论文摘要
网络上渗透的平均场理论模型提供了在节点或边缘去除下网络鲁棒性的分析估计。我们介绍了一个基于生成功能的新的平均场理论模型，其中包括有关每个节点本地邻居的树木风格的信息。我们表明，在测试广泛的现实网络数据范围的估算时，我们的新模型以预测准确性优于所有其他生成函数模型。我们将新模型的性能与最近引入的消息传递模型进行了比较，并提供了证据表明标准版本也表现出色，而“ Loopopy”版本仅在目标攻击策略上表现出色。但是，如我们所示，我们的模型实现的计算复杂性远低于消息传递算法的计算复杂性。我们提供的证据表明，所有讨论的模型在预测具有分散模块的高度模块化结构的网络时都很贫穷，这些网络的特征在于高混合时间，将其确定为渗透预测模型的一般限制。
Mean field theory models of percolation on networks provide analytic estimates of network robustness under node or edge removal. We introduce a new mean field theory model based on generating functions that includes information about the tree-likeness of each node's local neighbourhood. We show that our new model outperforms all other generating function models in prediction accuracy when testing their estimates on a wide range of real-world network data. We compare the new model's performance against the recently introduced message passing models and provide evidence that the standard version is also outperformed, while the `loopy' version is only outperformed on a targeted attack strategy. As we show, however, the computational complexity of our model implementation is much lower than that of message passing algorithms. We provide evidence that all discussed models are poor in predicting networks with highly modular structure with dispersed modules, which are also characterised by high mixing times, identifying this as a general limitation of percolation prediction models.
