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第193行： − 在众多网络产生故障的时候会出现一个重要现象，即某单个节点的单个故障可能会引起相邻节点的故障。继而发生少量故障导致更多故障的连锁反应，当最终规模达到近似相对于该网络规模的大量故障时，就发生了连锁故障现象。连锁故障有很多模型。这些模型在很多细节上并不相同，从电网故障到Twitter上的信息流的传播，研究者们对不同物理传播现象均尝试过进行建模，发现其中具有部分可共享的原理。每个模型都专注于某种传播方式或级联反应，有一些阈值确定节点何时将发生故障或被激活，进而有助于传播。并且通过定义某种机制，使得节点发生故障或激活时将产生定向传播。所有这些模型都预测了某种临界状态，其中潜在级联的大小分布与幂律是相匹配的，并且其指数是由基础网络的度指数唯一确定。由于模型之间的差异以及对结果的共识，我们被认为是潜在的现象是普遍的且与模型无关。+ − −
无编辑摘要
An important aspect of failures in many networks is that a single failure in one node might induce failures in neighboring nodes. When a small number of failures induces more failures, resulting in a large number of failures relative to the network size, a cascading failure has occurred. There are many models for cascading failures. These models differ in many details, and model different physical propagation phenomenon from power failures to information flow over Twitter, but have some shared principals. Each model focuses on some sort of propagation or cascade, there is some threshold determining when a node will fail or activate and contribute towards propagation, and there is some mechanism defined by which propagation will be directed when nodes fail or activate. All of these models predict some critical state, in which the distribution of the size of potential cascades matches a power law, and the exponent is uniquely determined by the degree exponent of the underlying network. Because of the differences in the models and the consensus of this result, we are led to believe the underlying phenomenon is universal and model-independent.
An important aspect of failures in many networks is that a single failure in one node might induce failures in neighboring nodes. When a small number of failures induces more failures, resulting in a large number of failures relative to the network size, a cascading failure has occurred. There are many models for cascading failures. These models differ in many details, and model different physical propagation phenomenon from power failures to information flow over Twitter, but have some shared principals. Each model focuses on some sort of propagation or cascade, there is some threshold determining when a node will fail or activate and contribute towards propagation, and there is some mechanism defined by which propagation will be directed when nodes fail or activate. All of these models predict some critical state, in which the distribution of the size of potential cascades matches a power law, and the exponent is uniquely determined by the degree exponent of the underlying network. Because of the differences in the models and the consensus of this result, we are led to believe the underlying phenomenon is universal and model-independent.
许多网络中的故障的一个重要方面是，一个节点中的单个故障可能会导致相邻节点中的故障。当少量故障导致更多故障，导致相对于网络规模的大量故障时，就发生了级联故障。级联故障有很多模型。这些模型在许多细节上都不同，并且对从电源故障到Twitter上的信息流的不同物理传播现象进行建模，但是具有一些共享的原理。每个模型都专注于某种传播或级联，有一些阈值确定节点何时将发生故障或激活，并有助于传播，并且定义了某种机制，通过该机制，当节点发生故障或激活时将定向传播。所有这些模型都预测了某种临界状态，其中潜在级联的大小分布与幂律相匹配，并且指数由基础网络的度指数唯一确定。由于模型之间的差异以及结果的共识，我们认为潜在的现象是普遍的且与模型无关。
For more detailed information on modeling cascading failures, see the [[global cascades model]] page.
For more detailed information on modeling cascading failures, see the [[global cascades model]] page.