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[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）motifs找到的相关资料是说：我们说motifs就是与随机网络相比出现次数较多的n-node subgraph结构。那么翻译成中文应该是什么呢？[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）

[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）motifs找到的相关资料是说：我们说motifs就是与随机网络相比出现次数较多的n-node subgraph结构。那么翻译成中文应该是什么呢？[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）

===Link analysis===

===链路分析===

Link analysis is a subset of network analysis, exploring associations between objects. An example may be examining the addresses of suspects and victims, the telephone numbers they have dialed and financial transactions that they have partaken in during a given timeframe, and the familial relationships between these subjects as a part of police investigation. Link analysis here provides the crucial relationships and associations between very many objects of different types that are not apparent from isolated pieces of information. Computer-assisted or fully automatic computer-based link analysis is increasingly employed by [[bank]]s and [[insurance]] agencies in [[fraud]] detection, by telecommunication operators in telecommunication network analysis, by medical sector in [[epidemiology]] and [[pharmacology]], in law enforcement [[Criminal procedure|investigation]]s, by [[search engine]]s for [[relevance]] rating (and conversely by the [[search engine spammer|spammers]] for [[spamdexing]] and by business owners for [[search engine optimization]]), and everywhere else where relationships between many objects have to be analyzed.

Link analysis is a subset of network analysis, exploring associations between objects. An example may be examining the addresses of suspects and victims, the telephone numbers they have dialed and financial transactions that they have partaken in during a given timeframe, and the familial relationships between these subjects as a part of police investigation. Link analysis here provides the crucial relationships and associations between very many objects of different types that are not apparent from isolated pieces of information. Computer-assisted or fully automatic computer-based link analysis is increasingly employed by [[bank]]s and [[insurance]] agencies in [[fraud]] detection, by telecommunication operators in telecommunication network analysis, by medical sector in [[epidemiology]] and [[pharmacology]], in law enforcement [[Criminal procedure|investigation]]s, by [[search engine]]s for [[relevance]] rating (and conversely by the [[search engine spammer|spammers]] for [[spamdexing]] and by business owners for [[search engine optimization]]), and everywhere else where relationships between many objects have to be analyzed.

链路分析是网络分析的一个子集，主要是探索对象之间的联系。 例如，作为警方调查的一部分，可以分析嫌疑人和受害人的地址、他们所拨打的电话号码和他们在一段时间内参与的金融交易，以及这些对象之间的家庭关系。链路分析提供了许多不同类型的对象之间的关键关系和相互联系，而这些在孤立的信息片段中是看不出来的。计算机辅助或全自动的基于计算机的链接分析越来越多地被银行和保险机构用于欺诈检测，被电信运营商用于电信网络分析，被医疗部门用于流行病学和药理学，被用于执法调查，被用于搜索引擎用于相关性评级(反之亦然，被滥发信息者用于滥发信息，及被业务负责人优化搜索引擎) 以及被用于任何有联系对象之间的分析。

链路分析是网络分析的一个子集，主要是探索对象之间的联系。 例如，作为警方调查的一部分，可以分析嫌疑人和受害人的地址、他们所拨打的电话号码和他们在一段时间内参与的金融交易，以及这些对象之间的家庭关系。链路分析提供了许多不同类型的对象之间的关键关系和相互联系，而这些在孤立的信息片段中是看不出来的。计算机辅助或全自动的基于计算机的链接分析越来越多地被银行和保险机构用于欺诈检测，被电信运营商用于电信网络分析，被医疗部门用于流行病学和药理学，被用于执法调查，被用于搜索引擎用于相关性评级(反之亦然，被滥发信息者用于滥发信息，及被业务负责人优化搜索引擎) 以及被用于任何有联系对象之间的分析。

 

====网络鲁棒性====

 

====Network robustness====

The structural robustness of networks<ref>{{cite book |title= Complex Networks: Structure, Robustness and Function |author1=R. Cohen |author2=S. Havlin |year= 2010 |publisher= Cambridge University Press |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_com_net.php}}</ref> is studied using [[percolation theory]]. When a critical fraction of nodes is removed the network becomes fragmented into small clusters. This phenomenon is called percolation<ref>{{cite book |title= Fractals and Disordered Systems |author1=A. Bunde |author2=S. Havlin |year= 1996 |publisher= Springer |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_fds.php}}</ref> and it represents an order-disorder type of [[phase transition]] with [[critical exponents]].

The structural robustness of networks<ref>{{cite book |title= Complex Networks: Structure, Robustness and Function |author1=R. Cohen |author2=S. Havlin |year= 2010 |publisher= Cambridge University Press |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_com_net.php}}</ref> is studied using [[percolation theory]]. When a critical fraction of nodes is removed the network becomes fragmented into small clusters. This phenomenon is called percolation<ref>{{cite book |title= Fractals and Disordered Systems |author1=A. Bunde |author2=S. Havlin |year= 1996 |publisher= Springer |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_fds.php}}</ref> and it represents an order-disorder type of [[phase transition]] with [[critical exponents]].

利用渗流理论研究了网络[34]的结构鲁棒性。 当节点的一个临界部分被移除时，网络变得支离破碎。 这种现象被称为渗流[35] ，它代表了一种从有序-无序的临界指数的相变类型。

利用渗流理论研究了网络[34]的结构鲁棒性。 当节点的一个临界部分被移除时，网络变得支离破碎。 这种现象被称为渗流[35] ，它代表了一种从有序-无序的临界指数的相变类型。

 

====流行病分析====

====Pandemic analysis====

 

The [[SIR model]] is one of the most well known algorithms on predicting the spread of global pandemics within an infectious population.

The [[SIR model]] is one of the most well known algorithms on predicting the spread of global pandemics within an infectious population.

SIR模型是预测全球传染病在感染人群中传播的最著名的算法之一。

SIR模型是预测全球传染病在感染人群中传播的最著名的算法之一。

=====Susceptible to infected=====

=====易感人群=====

 

: <math>S = \beta\left(\frac 1 N \right)</math>

: <math>S = \beta\left(\frac 1 N \right)</math>

这个公式描述的是人群中每个易感单元的感染“力”，其中 {{math|β}} 代表的是疾病的传播概率。

这个公式描述的是人群中每个易感单元的感染“力”，其中 {{math|β}} 代表的是疾病的传播概率。

To track the change of those susceptible in an infectious population:

To track the change of those susceptible in an infectious population:

跟踪人群中易感人数随时间的变化：

跟踪人群中易感人数随时间的变化：

: <math>\Delta S = \beta \times S {1\over N} \, \Delta t</math>

: <math>\Delta S = \beta \times S {1\over N} \, \Delta t</math>

=====Infected to recovered=====

=====从感染到康复=====

 

: <math>\Delta I = \mu I \, \Delta t</math>

: <math>\Delta I = \mu I \, \Delta t</math>

随着时间的推移，感染人数的波动幅度为: 以<math>\mu</math>来表示特定的恢复率，移除的平均感染期记为<math>{1\over \tau}</math>，感染个体的数量 <math>I</math>，以及时间的变化<math>\Delta t</math>。

随着时间的推移，感染人数的波动幅度为: 以<math>\mu</math>来表示特定的恢复率，移除的平均感染期记为<math>{1\over \tau}</math>，感染个体的数量 <math>I</math>，以及时间的变化<math>\Delta t</math>。

就 SIR 模型而言，被流行病感染的人口数量，取决于数学<math>R_0</math>基本传染数的数值，是指被感染个体能感染普通人群的人数。比如<math>R_0 = 3</math>，意味着一个感染者平均感染3个未感染者。

就 SIR 模型而言，被流行病感染的人口数量，取决于数学<math>R_0</math>基本传染数的数值，是指被感染个体能感染普通人群的人数。比如<math>R_0 = 3</math>，意味着一个感染者平均感染3个未感染者。

: <math>R_0 = \beta\tau = {\beta\over\mu}</math>

: <math>R_0 = \beta\tau = {\beta\over\mu}</math>

====Web link analysis====

====网络连接分析====

 

Several [[Web search]] [[ranking]] algorithms use link-based centrality metrics, including (in order of appearance) [[Massimo Marchiori|Marchiori]]'s [[Hyper Search]], [[Google]]'s [[PageRank]], Kleinberg's [[HITS algorithm]], the [[CheiRank]] and [[TrustRank]] algorithms. Link analysis is also conducted in information science and communication science in order to understand and extract information from the structure of collections of web pages. For example, the analysis might be of the interlinking between politicians' web sites or blogs.

Several [[Web search]] [[ranking]] algorithms use link-based centrality metrics, including (in order of appearance) [[Massimo Marchiori|Marchiori]]'s [[Hyper Search]], [[Google]]'s [[PageRank]], Kleinberg's [[HITS algorithm]], the [[CheiRank]] and [[TrustRank]] algorithms. Link analysis is also conducted in information science and communication science in order to understand and extract information from the structure of collections of web pages. For example, the analysis might be of the interlinking between politicians' web sites or blogs.

一些网络搜索排名算法使用基于链接的中心度矩阵，包括Marchiori的 Hyper Search、 Google 的 PageRank、 Kleinberg 的 HITS 算法、 CheiRank 和 TrustRank 算法。 在信息科学和传播学中也进行链接分析，以便从网页的结构中理解和提取信息。 例如，可以分析政客的网站或博客之间的相互联系。

一些网络搜索排名算法使用基于链接的中心度矩阵，包括Marchiori的 Hyper Search、 Google 的 PageRank、 Kleinberg 的 HITS 算法、 CheiRank 和 TrustRank 算法。 在信息科学和传播学中也进行链接分析，以便从网页的结构中理解和提取信息。 例如，可以分析政客的网站或博客之间的相互联系。

[[PageRank]] works by randomly picking "nodes" or websites and then with a certain probability, "randomly jumping" to other nodes. By randomly jumping to these other nodes, it helps PageRank completely traverse the network as some webpages exist on the periphery and would not as readily be assessed.

[[PageRank]] works by randomly picking "nodes" or websites and then with a certain probability, "randomly jumping" to other nodes. By randomly jumping to these other nodes, it helps PageRank completely traverse the network as some webpages exist on the periphery and would not as readily be assessed.

Each node, <math>x_i</math>, has a PageRank as defined by the sum of pages <math>j</math> that link to <math>i</math> times one over the outlinks or "out-degree" of <math>j</math> times the "importance" or PageRank of <math>j</math>.

Each node, <math>x_i</math>, has a PageRank as defined by the sum of pages <math>j</math> that link to <math>i</math> times one over the outlinks or "out-degree" of <math>j</math> times the "importance" or PageRank of <math>j</math>.

PageRank算法的工作原理是随机选择“节点”或网站，然后以一定的概率“随机跳转”到其他节点。 通过随机跳转到这些其他节点，它帮助 PageRank算法完全遍历网络，因为可能一些不容易被跳转到的边缘网站。

PageRank算法的工作原理是随机选择“节点”或网站，然后以一定的概率“随机跳转”到其他节点。通过随机跳转到这些其他节点，它帮助 PageRank算法完全遍历网络，因为可能一些不容易被跳转到的边缘网站。

: <math>x_i = \sum_{j\rightarrow i}{1\over N_j}x_j^{(k)}</math>

: <math>x_i = \sum_{j\rightarrow i}{1\over N_j}x_j^{(k)}</math>

======Random jumping======

======随机跳转======

As explained above, PageRank enlists random jumps in attempts to assign PageRank to every website on the internet. These random jumps find websites that might not be found during the normal search methodologies such as [[Breadth-First Search]] and [[Depth-First Search]].

As explained above, PageRank enlists random jumps in attempts to assign PageRank to every website on the internet. These random jumps find websites that might not be found during the normal search methodologies such as [[Breadth-First Search]] and [[Depth-First Search]].

正如上面解释的那样，试图通过随机跳转，为互联网上的每个网站分配网页排名。通过随机跳转可以找到一些在正常的搜索方法（如广度优先搜索和深度优先搜索）中找不到的边缘网站。

正如上面解释的那样，试图通过随机跳转，为互联网上的每个网站分配网页排名。通过随机跳转可以找到一些在正常的搜索方法（如广度优先搜索和深度优先搜索）中找不到的边缘网站。

The first is <math>\alpha</math>, or the probability that a random jump will occur. Contrasting is the "damping factor", or <math>1 - \alpha</math>.

The first is <math>\alpha</math>, or the probability that a random jump will occur. Contrasting is the "damping factor", or <math>1 - \alpha</math>.

第一个字母<math>\alpha</math>，代表的是随机跳转发生的概率。与此相对的是阻尼因子，对应的是<math>1 - \alpha</math>。

第一个字母<math>\alpha</math>，代表的是随机跳转发生的概率。与此相对的是阻尼因子，对应的是<math>1 - \alpha</math>。

: <math>R{(p)} = {\alpha\over N} + (1 - \alpha) \sum_{j\rightarrow i} {1\over N_j} x_j^{(k)}</math>

: <math>R{(p)} = {\alpha\over N} + (1 - \alpha) \sum_{j\rightarrow i} {1\over N_j} x_j^{(k)}</math>