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第411行： − 观察不断演化的网络最常用的方法是把它们看作连续的静态网络。这可以概念化为个人静态图像组成一个电影。许多简单的参数用来描述一个静态网络(节点数、边、路径长度、连接组件) ，或者用来描述图中的特定节点，比如链接数或集聚系数。然后可以使用信号处理概念将这些属性分别作为时间序列进行研究+ + 第441行：
第442行： − 例如，我们可以通过查看网络的连续快照并计算每个快照中的链接数量，来跟踪每分钟建立到服务器的链接数量。 第449行：
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→Treat evolving networks as successive snapshots of a static network
|volume=In Press |date=2015}}</ref> have shown that when individuals in a system display varying levels of rationality, improving the overall system rationality might be an evolutionary reason for the emergence of scale-free networks. They demonstrated this by applying evolutionary pressure on an initially random network which simulates a range of classic games, so that the network converges towards Nash equilibria while being allowed to re-wire. The networks become increasingly scale-free during this process.
|volume=In Press |date=2015}}</ref> have shown that when individuals in a system display varying levels of rationality, improving the overall system rationality might be an evolutionary reason for the emergence of scale-free networks. They demonstrated this by applying evolutionary pressure on an initially random network which simulates a range of classic games, so that the network converges towards Nash equilibria while being allowed to re-wire. The networks become increasingly scale-free during this process.
===Treat evolving networks as successive snapshots of a static network===
===Treat evolving networks as successive snapshots of a static network 视演化网络为连续的静态网络快照===
The most common way to view evolving networks is by considering them as successive static networks. This could be conceptualized as the individual still images which compose a [[video|motion picture]]. Many simple parameters exist to describe a static network (number of nodes, edges, path length, connected components), or to describe specific nodes in the graph such as the number of links or the clustering coefficient. These properties can then individually be studied as a time series using signal processing notions.<ref name=EvolvingNetworksPDF>{{Cite journal
The most common way to view evolving networks is by considering them as successive static networks. This could be conceptualized as the individual still images which compose a [[video|motion picture]]. Many simple parameters exist to describe a static network (number of nodes, edges, path length, connected components), or to describe specific nodes in the graph such as the number of links or the clustering coefficient. These properties can then individually be studied as a time series using signal processing notions.<ref name=EvolvingNetworksPDF>{{Cite journal
The most common way to view evolving networks is by considering them as successive static networks. This could be conceptualized as the individual still images which compose a motion picture. Many simple parameters exist to describe a static network (number of nodes, edges, path length, connected components), or to describe specific nodes in the graph such as the number of links or the clustering coefficient. These properties can then individually be studied as a time series using signal processing notions.<ref name=EvolvingNetworksPDF>{{Cite journal
The most common way to view evolving networks is by considering them as successive static networks. This could be conceptualized as the individual still images which compose a motion picture. Many simple parameters exist to describe a static network (number of nodes, edges, path length, connected components), or to describe specific nodes in the graph such as the number of links or the clustering coefficient. These properties can then individually be studied as a time series using signal processing notions.<ref name=EvolvingNetworksPDF>{{Cite journal
观察不断演化的网络最常用的方法是把它们看作连续的静态网络。这可以概念化为组成电影的一个个静态图像。有许多简单的参数可以描述一个静态网络（节点数、边、路径长度、连通子图），或者描述图中的特定节点，比如链接数或集聚系数。然后可以使用信号处理概念将这些属性分别作为时间序列进行研究。例如，我们可以通过查看网络的连续快照并计算每个快照中的链接数量，来跟踪每分钟建立到服务器的链接数量。
| url = http://liris.cnrs.fr/Documents/Liris-3669.pdf
| url = http://liris.cnrs.fr/Documents/Liris-3669.pdf
|display-authors=etal}}</ref> For example, we can track the number of links established to a server per minute by looking at the successive snapshots of the network and counting these links in each snapshot.
|display-authors=etal}}</ref> For example, we can track the number of links established to a server per minute by looking at the successive snapshots of the network and counting these links in each snapshot.
Unfortunately, the analogy of snapshots to a motion picture also reveals the main difficulty with this approach: the time steps employed are very rarely suggested by the network and are instead arbitrary. Using extremely small time steps between each snapshot preserves resolution, but may actually obscure wider trends which only become visible over longer timescales. Conversely, using larger timescales loses the temporal order of events within each snapshot. Therefore, it may be difficult to find the appropriate timescale for dividing the evolution of a network into static snapshots.
Unfortunately, the analogy of snapshots to a motion picture also reveals the main difficulty with this approach: the time steps employed are very rarely suggested by the network and are instead arbitrary. Using extremely small time steps between each snapshot preserves resolution, but may actually obscure wider trends which only become visible over longer timescales. Conversely, using larger timescales loses the temporal order of events within each snapshot. Therefore, it may be difficult to find the appropriate timescale for dividing the evolution of a network into static snapshots.
不幸的是，快照与电影的类比也揭示了这种方法的主要困难: 使用的时间步骤很少由网络建议，而是任意的。在每个快照之间使用极小的时间步骤可以保持分辨率，但实际上可能掩盖了只有在较长时间尺度下才能看到的更广泛的趋势。相反，使用较大的时间尺度会失去每个快照中事件的时间顺序。因此，可能很难找到合适的时间尺度来将网络的演变划分为静态快照。
不幸的是，快照与电影的类比也揭示了这种方法的主要困难: 使用的时间步骤很少由网络给出，而是任意的。在每个快照之间使用极小的时间步骤可以保持分辨率，但实际上可能掩盖了只有在较长时间尺度下才能看到的更广泛的趋势。相反，使用较大的时间尺度会失去每个快照中事件的时间顺序。因此，可能很难找到合适的时间尺度来将网络的演变划分为静态快照。
===Define dynamic properties===
===Define dynamic properties===