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第191行： + + + − 我们可以预期这个过程会在网络的相当一部分中传播谣言。但是请注意，如果我们在一个节点周围有一个强大的[[群聚系数|本地集群（local clustering）]] ，那么可能发生的情况是，许多节点成为传播者，并且有作为传播者的邻居。然后，每次我们选择其中之一（辟谣），他们将恢复，并可以消除谣言传播。另一方面，如果我们有一个[[Small-world network|小世界（small world）]]的网络，也就是说，在一个网络中，两个随机选择的节点之间的最短路径比预期的要小得多，我们可以预期，谣言会传播得很广。 −
→小世界中的传染病模型Epidemic Models in Small-World Network
===小世界中的传染病模型Epidemic Models in Small-World Network ===
===小世界中的传染病模型Epidemic Models in Small-World Network ===
We would expect that this process spreads the rumor throughout a considerable fraction of the network. Note however that if we have a strong [[Clustering coefficient|local clustering]] around a node, what can happen is that many nodes become spreaders and have neighbors who are spreaders. Then, every time we pick one of those, they will recover and can extinguish the rumor spread. On the other hand, if we have a network that is [[Small-world Network|Small World]], that is, a network in which the shortest path between two randomly chosen nodes is much smaller than that one would expect, we can expect the rumor spread far away.
We would expect that this process spreads the rumor throughout a considerable fraction of the network. Note however that if we have a strong [[Clustering coefficient|local clustering]] around a node, what can happen is that many nodes become spreaders and have neighbors who are spreaders. Then, every time we pick one of those, they will recover and can extinguish the rumor spread. On the other hand, if we have a network that is [[Small-world Network|Small World]], that is, a network in which the shortest path between two randomly chosen nodes is much smaller than that one would expect, we can expect the rumor spread far away.
我们可以预期这个过程会在网络的相当一部分中传播谣言。但是请注意，如果我们在一个节点周围有一个强大的[[群聚系数|本地集群（local clustering）]] ，那么可能发生的情况是，许多节点成为传播者，并且有作为传播者的邻居。然后，每次我们选择其中之一（辟谣），他们将恢复，并可以消除谣言传播。另一方面，如果我们有一个[[Small-world network|小世界（small world）]]的网络，也就是说，在一个网络中，两个随机选择的节点之间的最短路径比预期的要小得多，我们可以预期，谣言会传播得很广。
Also we can compute the final number of people who once spread the news, this is given by<br />
Also we can compute the final number of people who once spread the news, this is given by<br />
<math>r_\infty=1-e^{-({\alpha +\beta \over \beta})r_\infty}</math> <br />
<math>r_\infty=1-e^{-({\alpha +\beta \over \beta})r_\infty}</math> <br />
In networks the process that does not have a threshold in a well mixed population, exhibits a clear cut phase-transition in small worlds. The following graph illustrates the asymptotic value of <math>r_\infty</math> as a function of the rewiring probability <math>p</math>.
In networks the process that does not have a threshold in a well mixed population, exhibits a clear cut phase-transition in small worlds. The following graph illustrates the asymptotic value of <math>r_\infty</math> as a function of the rewiring probability <math>p</math>.