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'''Complex contagion''' is the phenomenon in [[social network]]s in which multiple sources of exposure to an innovation are required before an individual adopts the change of behavior.<ref name="CENTOLA" /> It differs from simple contagion in that unlike a disease, it may not be possible for the innovation to spread after only one incident of contact with an infected neighbor. The spread of complex contagion across a network of people may depend on many social and economic factors; for instance, how many of one's friends adopt the new idea as well as how many of them cannot influence the individual, as well as their own disposition in embracing change.

Complex contagion is the phenomenon in social networks in which multiple sources of exposure to an innovation are required before an individual adopts the change of behavior. It differs from simple contagion in that unlike a disease, it may not be possible for the innovation to spread after only one incident of contact with an infected neighbor. The spread of complex contagion across a network of people may depend on many social and economic factors; for instance, how many of one's friends adopt the new idea as well as how many of them cannot influence the individual, as well as their own disposition in embracing change.

复杂传染是社会网络中的一种现象，在这种社会网络中，一个人在采取行为改变之前，需要有多个接触创新的渠道。它不同于简单的传染病，不同于疾病，创新不可能仅仅在与受感染的邻居发生一次接触后传播。复杂的传染病在一个人际网络中的传播可能取决于许多社会和经济因素; 例如，一个人的朋友中有多少人接受了新观念，有多少人不能影响个人，以及他们接受改变的倾向。

==Mechanisms==

'''''Complex Contagion and the Weakness of Long Ties''''' by [[Damon Centola]] of [[University of Pennsylvania]] and [[Michael Macy]] of [[Cornell University]] found that information and disease spread as “simple contagions”, requiring only one contact for transmission, while behaviors typically spread as “complex contagions”, requiring multiples sources of reinforcement to induce adoption. Centola’s work builds on [[Mark Granovetter|Granovetter’s work]] on the strength of weak ties and threshold models of collective behavior, as well as [[Duncan J. Watts|Duncan Watts]] and [[Steven Strogatz|Steve Strogatz’s]] work on small world networks.<ref>http://science.sciencemag.org/content/329/5996/1194</ref> Centola and Macy show that the weak ties and small worlds networks are both very good for spreading simple contagions. However, for complex contagions, weak ties and small worlds can slow diffusion.

Complex Contagion and the Weakness of Long Ties by Damon Centola of University of Pennsylvania and Michael Macy of Cornell University found that information and disease spread as “simple contagions”, requiring only one contact for transmission, while behaviors typically spread as “complex contagions”, requiring multiples sources of reinforcement to induce adoption. Centola’s work builds on Granovetter’s work on the strength of weak ties and threshold models of collective behavior, as well as Duncan Watts and Steve Strogatz’s work on small world networks. Centola and Macy show that the weak ties and small worlds networks are both very good for spreading simple contagions. However, for complex contagions, weak ties and small worlds can slow diffusion.

宾夕法尼亚大学的 Damon Centola 和康奈尔大学的 Michael Macy 发现，信息和疾病的传播是简单的传染，只需要一个接触来传播，而行为通常以复杂传染的形式传播，需要多个来源的强化来诱导接受。的工作建立在 Granovetter 的弱关系的力量和集体行为的阈值模型的工作上，以及 Duncan Watts 和 Steve Strogatz 的小世界网络的工作上。和 Macy 指出弱关系和小世界网络都非常有利于传播简单传染。然而，对于复杂的传染，弱关系和小世界可以减缓扩散。

Centola and Macy suggest four mechanisms of complex contagion. These properties explain the need for multiple exposures in the spread of contagion:

Centola and Macy suggest four mechanisms of complex contagion. These properties explain the need for multiple exposures in the spread of contagion:

森托拉和梅西提出了四种复杂传染机制。这些特性解释了在传染蔓延过程中需要多重风险敞口的原因:

#''Strategic complementarity.'' Many innovations are costly, especially for early adopters but less so for those who wait. The same holds for participation in [[collective action]].

Strategic complementarity. Many innovations are costly, especially for early adopters but less so for those who wait. The same holds for participation in collective action.

战略互补性。许多创新成本高昂，尤其是对于早期采用者，但对于那些等待的人来说成本较低。参与集体行动也是如此。

#''Credibility.'' Innovations often lack credibility until adopted by neighbors. Hearing the same story from different people makes it seem less likely that surprising information is nothing more than the fanciful invention of the informant.

Credibility. Innovations often lack credibility until adopted by neighbors. Hearing the same story from different people makes it seem less likely that surprising information is nothing more than the fanciful invention of the informant.

可信度。在被邻居采用之前，创新往往缺乏可信度。从不同的人那里听到同样的故事，使得令人惊讶的信息不过是线人幻想出来的东西的可能性降低了。

#''Legitimacy.'' Knowing that a movement exists or that a collective action will take place is rarely sufficient to induce bystanders to join in. Having several close friends participate in an event often greatly increases an individual’s likelihood of also joining, especially for high-risk [[social movement]]s. Innovators risk being shunned as [[deviant]]s until there is a critical mass of early adopters, and non-adopters are likely to challenge the legitimacy of the innovation.

Legitimacy. Knowing that a movement exists or that a collective action will take place is rarely sufficient to induce bystanders to join in. Having several close friends participate in an event often greatly increases an individual’s likelihood of also joining, especially for high-risk social movements. Innovators risk being shunned as deviants until there is a critical mass of early adopters, and non-adopters are likely to challenge the legitimacy of the innovation.

合法性。知道一个运动的存在或者一个集体行动将要发生并不足以吸引旁观者加入。有几个亲密的朋友参加一个活动通常会大大增加一个人加入的可能性，特别是对于高风险的社会运动。创新者冒着被当作异类而回避的风险，直到有足够数量的早期采用者，而非采用者可能会挑战创新的合法性。

#''[[Emotional contagion]].'' Most theoretical models of [[collective behavior]] – from [[Action theory (sociology)|action theory]] to [[threshold model]]s to [[cybernetics]] share the basic assumption that there are expressive and symbolic impulses in [[human behavior]] that can be communicated and amplified in spatially and socially concentrated gatherings.<ref name="CENTOLA" />

Emotional contagion. Most theoretical models of collective behavior – from action theory to threshold models to cybernetics share the basic assumption that there are expressive and symbolic impulses in human behavior that can be communicated and amplified in spatially and socially concentrated gatherings.

情绪感染。大多数关于集体行为的理论模型——从行动理论到阈值模型到控制论——都有一个共同的基本假设，即人类行为中存在表达冲动和象征冲动，这些冲动可以在空间和社会集中的集会中得到沟通和放大。

==Contested vs. uncontested==

;Uncontested

Uncontested

无争议的

:The spread of the contagion is dependent solely on the number of people you are connected to who are different from your own state. You are not hindered whatsoever by the number of people in the same state as you. Generally, the more neighbors an individual has, the greater the chance of the individual adopting the innovation if the spread is uncontested.<ref name="CENTOLA" />

The spread of the contagion is dependent solely on the number of people you are connected to who are different from your own state. You are not hindered whatsoever by the number of people in the same state as you. Generally, the more neighbors an individual has, the greater the chance of the individual adopting the innovation if the spread is uncontested.

传染的传播完全取决于与你有联系的人的数量，这些人与你所在的州不同。你不会因为和你处于同一状态的人的数量而受到任何阻碍。一般来说，一个人的邻居越多，如果创新的传播是无争议的，那么这个人采用创新的机会就越大。

;Contested

Contested

竞逐

:The spread of the contagion is dependent on both the adamancy of those who are in a different state from your own as well as the countervailing influence of those who share your current state. In this case, the more neighbors an individual has, the smaller the chance of the individual adopting the innovation.<ref name="CENTOLA" />

The spread of the contagion is dependent on both the adamancy of those who are in a different state from your own as well as the countervailing influence of those who share your current state. In this case, the more neighbors an individual has, the smaller the chance of the individual adopting the innovation.

传染病的传播既取决于那些与你处于不同状态的人的坚强，也取决于那些与你处于同一状态的人的抵消性影响。在这种情况下，一个人的邻居越多，他采用创新的机会就越小。

==Diffusion and cascading behaviors in networks==

{{see also|Global cascades model}}

Consider a [[Graph (discrete mathematics)|graph]] of any reasonable size. Node v’s neighbors can be split into two sets: Set A contains v's neighbors who have adopted a new behavior and B is the set of those behaving conservatively. Node v will only adopt the behavior of those in A if at least a ''q'' fraction of neighbors follow behavior A.<ref name="EK" />

Consider a graph of any reasonable size. Node v’s neighbors can be split into two sets: Set A contains v's neighbors who have adopted a new behavior and B is the set of those behaving conservatively. Node v will only adopt the behavior of those in A if at least a q fraction of neighbors follow behavior A.

考虑一个合理大小的图表。节点 v 的邻居可以分成两组: 设置 a 包含已经采用新行为的 v 邻居，设置 b 是行为保守的邻居。如果至少有一部分邻居遵循行为 a，那么 Node v 只会采用 a 中的行为。

*if ''q'' is small, the behavior is easily adopted and easily spread

*if ''q'' is large, B is an attractive behavior and it takes more friends to engage in A before v will switch.<ref name="EK" />

;Cascading – diffusion over the entire network : Consider a set of initial adopters who start with a new behavior A, while every other node starts with behavior B. Nodes then repeatedly evaluate the decision to switch from B to A using a threshold of ''q''. If the resulting cascade of adoptions of A eventually causes every node to switch from B to A, then we say that the set of initial adopters causes a complete cascade at threshold ''q''. Clusters of density ''d'' > 1 − ''q'' are obstacles to cascades across the entire network.<ref name="EK" />

Cascading – diffusion over the entire network : Consider a set of initial adopters who start with a new behavior A, while every other node starts with behavior B. Nodes then repeatedly evaluate the decision to switch from B to A using a threshold of q. If the resulting cascade of adoptions of A eventually causes every node to switch from B to A, then we say that the set of initial adopters causes a complete cascade at threshold q. Clusters of density d > 1 − q are obstacles to cascades across the entire network.

整个网络的级联扩散: 考虑一组以新行为 a 开始的初始采用者，而其他每个节点以行为 b 开始。然后使用 q 的阈值重复计算从 b 切换到 a 的决策。如果 a 的采用产生的级联最终导致每个节点从 b 切换到 a，那么我们说，初始采用者的集合在阈值 q 处导致了一个完整的级联。

==Application and examples==

Many of our interactions with the rest of the world happen at a local, rather than a global, level – we often don't care as much about the full population's decisions as about the decisions made by friends and colleagues. For example, in a work setting we may choose technology to be compatible with the people we directly collaborate with, rather than the universally most popular technology. Similarly, we may adopt political views that are aligned with those of our friends, even if they are nationally in the minority.<ref name="EK" />

Many of our interactions with the rest of the world happen at a local, rather than a global, level – we often don't care as much about the full population's decisions as about the decisions made by friends and colleagues. For example, in a work setting we may choose technology to be compatible with the people we directly collaborate with, rather than the universally most popular technology. Similarly, we may adopt political views that are aligned with those of our friends, even if they are nationally in the minority.

我们与世界其他地方的许多互动发生在一个本地层面，而不是一个全球层面——我们往往不像关心朋友和同事的决定那样关心全体人民的决定。例如，在工作环境中，我们可能会选择与我们直接合作的人兼容的技术，而不是最普遍流行的技术。同样，我们可能采取与我们的朋友一致的政治观点，即使他们在全国范围内属于少数派。

===Examples ===

*The credibility of an [[urban legend]]

*Willingness to participate in migration – (participating in a collective action)

*Incentives to exit formal gatherings

*What clothing to wear, hairstyle to adopt, and what part of the body to pierce.<ref name="CENTOLA" />

===Examples of simple contagion===

*The spread of disease

*The spread of information<ref name="CENTOLA" />

==See also==

*[[Emotional contagion]]

*[[Mark Granovetter]]

*[[Graph theory]]

==References==

{{Reflist|refs=

{{Reflist|refs=

{通货再膨胀 | 参考文献

<ref name="CENTOLA">Centola, Damon; Macy, Michael. [ftp://hive.soc.cornell.edu/mwm14/webpage/WLT.pdf "Complex Contagions and the Weakness of Long Ties."]{{dead link|date=August 2017 |bot=InternetArchiveBot |fix-attempted=yes }} University of Chicago, 2007.</ref>

<ref name="EK">Easley, David; Kleinberg, Jon. [http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf ''Networks, Crowds, and Markets: Reasoning about a Highly Connected World.''] Cambridge University Press, 2010.</ref>

}}

}}

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{{Social networking}}

[[Category:Social networks]]

Category:Social networks

分类: 社交网络

<noinclude>

<small>This page was moved from [[wikipedia:en:Complex contagion]]. Its edit history can be viewed at [[复杂传染/edithistory]]</small></noinclude>

[[Category:待整理页面]]

@@ 第79行： / 第79行： @@
 关于社交媒体上出现“信息雪崩”统计定律，至少目前为止还没有一个在各个系统都表现得足够鲁棒的实证研究。而对于信息传播来说，完全不同的过程很可能背后存在着合理的驱动机制。最近在 ''Nature Communications'' 上发表的一篇文献，分析了来自Titter、Telegram、微博等社交平台的近亿个时间序列，超过将近10年的时间窗口，展示了社交媒体上的信息传播过程的普适性和临界性。其中普适性体现在，无论具体系统的细节如何，我们都可以在不同的系统上观察到宏观尺度的统一模式；临界性则来自于信息雪崩持续时间和规模的幂律分布，相应的超规模关系（hyperscaling relations）。文章在数据上的统计检验指出，社交媒体上信息传播过程是简单传播规则和复杂传播规则的结合，并且还指出这个过程的复杂性和传播信息中包含的语义内容有关。
-----本中文词条由AvecSally参与审校编辑，如有问题，欢迎在讨论页面留言。
+----本中文词条由AvecSally审校编辑，如有问题，欢迎在讨论页面留言。
 '''本词条内容源自wikipedia及公开资料，遵守 CC3.0协议。'''

←上一版本		2022年4月22日 (五) 07:22的版本
第60行：		第60行：
	== 参考文献 ==		== 参考文献 ==
	<references />		<references />
		+
		+	== 编者推荐 ==
		+	[[文件:Sir.jpg\|缩略图\|[https://swarma.org/?p=18758 超越SIR模型：信息与疾病传播的复杂建模]]]
		+
		+	=== 集智文章推荐 ===
		+
		+	* [https://swarma.org/?p=18758 超越SIR模型：信息与疾病传播的复杂建模]
		+
		+	社会对个体某种行为的赞扬与否定这一行为称为社会强化效应，在信息传播中也起到了重要作用，这意味着信息传播的概率是会变化的。疾病传播也有类似效应。近期发表在Nature Physics上的一篇论文，提出了一个复杂传播模型，并建议针对网络上的信息疾病传播研究，应当优先使用复杂模型，而非SIR等简单模型。
		+
		+	* [https://swarma.org/?p=28065 研究速递：图神经网络预测复杂网络中的传播现象]
		+
		+	如何预测复杂网络中的传播演进，大部分模型依赖简化的假设。近日，一篇发表于 ''Nature Communications'' 的论文“基于复杂网络中传染动态的深度学习模型”，基于时间序列，使用图神经网络，在去年新冠在西班牙的传播数据中证明了该方法的适用性。从而为复杂网络中传播现象的研究，指出了新的方向。
		+	[[文件:Criticality.png\|缩略图\|[https://swarma.org/?p=33330 社交媒体信息传播的普适规律：临界性与复杂性]]]
		+
		+	* [https://swarma.org/?p=33330 社交媒体信息传播的普适规律：临界性与复杂性]
		+
		+	关于社交媒体上出现“信息雪崩”统计定律，至少目前为止还没有一个在各个系统都表现得足够鲁棒的实证研究。而对于信息传播来说，完全不同的过程很可能背后存在着合理的驱动机制。最近在 ''Nature Communications'' 上发表的一篇文献，分析了来自Titter、Telegram、微博等社交平台的近亿个时间序列，超过将近10年的时间窗口，展示了社交媒体上的信息传播过程的普适性和临界性。其中普适性体现在，无论具体系统的细节如何，我们都可以在不同的系统上观察到宏观尺度的统一模式；临界性则来自于信息雪崩持续时间和规模的幂律分布，相应的超规模关系（hyperscaling relations）。文章在数据上的统计检验指出，社交媒体上信息传播过程是简单传播规则和复杂传播规则的结合，并且还指出这个过程的复杂性和传播信息中包含的语义内容有关。
		+
		+	----本中文词条由AvecSally参与审校编辑，如有问题，欢迎在讨论页面留言。
		+
		+	'''本词条内容源自wikipedia及公开资料，遵守 CC3.0协议。'''

@@ 第1行： / 第1行： @@
-'''复杂传染 Complex contagion'''是社会网络中的一种现象，在社会网络中，一个人或一个个体需要受到多种不同的影响，会对自身行为采取改变。 不同于疾病的简单传染，邻居单一的一次接触并不会使其成功传染，复杂传染的传播需要人群网络中复杂交错的社会和经济因素。例如，一个人有多少个朋友接受这个新想法，以及这些朋友能不能影响这个人，以及他们自己接受改变的倾向。
+'''复杂传染 Complex contagion'''是社会网络中的一种现象，在社会网络中，一个人或一个个体需要受到多种不同的影响，会对自身行为采取改变。<ref>Centola, Damon; Macy, Michael. "Complex Contagions and the Weakness of Long Ties." University of Chicago, 2007.</ref> 不同于疾病的简单传染，邻居单一的一次接触并不会使其成功传染，复杂传染的传播需要人群网络中复杂交错的社会和经济因素。例如，一个人有多少个朋友接受这个新想法，以及这些朋友能不能影响这个人，以及他们自己接受改变的倾向。
 == 传播机制 ==
-宾夕法尼亚大学的Damon Centola和康奈尔大学的Michael Macy合著的《复杂传染和长纽带的弱点'''''Complex Contagion and the Weakness of Long Ties''''' 》发现，信息和疾病以“简单传染”的方式传播，只需要一次接触就能传播，而其传播行为通常以“复杂传染”的方式传播，这通常需要多种不同的来源的强化来使其感染。Centola的工作建立在Granovetter关于弱联系的力量和集体行为的阈值模型的工作，以及邓肯·瓦茨 Duncan J. Watts和斯蒂文·斯特罗加茨 Steven H. Strogatz关于小世界网络的工作的基础上。Centola和Macy表明，弱联系和小世界网络都非常有利于简单传播的传染病。然而，对于复杂传染，弱联系和小世界可以有效减缓扩散。
+宾夕法尼亚大学的Damon Centola和康奈尔大学的Michael Macy合著的《复杂传染和长纽带的弱点'''''Complex Contagion and the Weakness of Long Ties''''' 》发现，信息和疾病以“简单传染”的方式传播，只需要一次接触就能传播，而其传播行为通常以“复杂传染”的方式传播，这通常需要多种不同的来源的强化来使其感染。Centola的工作建立在Granovetter关于弱联系的力量和集体行为的阈值模型的工作，以及邓肯·瓦茨 Duncan J. Watts和斯蒂文·斯特罗加茨 Steven H. Strogatz关于小世界网络<ref>Centola, Damon (2010). "The Spread of Behavior in an Online Social Network Experiment". ''Science''. '''329''' (5996): 1194–1197. Bibcode:2010Sci...329.1194C. doi:10.1126/science.1185231. <nowiki>PMID 20813952</nowiki>. S2CID 3265637.</ref>的工作的基础上。Centola和Macy表明，弱联系和小世界网络都非常有利于简单传播的传染病。然而，对于复杂传染，弱联系和小世界可以有效减缓扩散。
 Centola和Macy梅西提出了四种复杂传染的机制，这些性质解释了在传染过程中为什么需要多次不同来源的强化：
@@ 第44行： / 第44行： @@
 == 应用案例 ==
-许多互动发生在局部层面，而不是整体层面——我们往往不像关心朋友和同事所做的决定那样关心整个群体的决定。例如，在工作环境中，我们可能会选择与我们直接合作的人兼容的技术，而不是全局流行的技术。同样，我们可能会采取与我们的朋友一致的政治观点，即使他们属于少数群体。
+许多互动发生在局部层面，而不是整体层面——我们往往不像关心朋友和同事所做的决定那样关心整个群体的决定。例如，在工作环境中，我们可能会选择与我们直接合作的人兼容的技术，而不是全局流行的技术。同样，我们可能会采取与我们的朋友一致的政治观点，即使他们属于少数群体。<ref>Easley, David; Kleinberg, Jon. ''Networks, Crowds, and Markets: Reasoning about a Highly Connected World.'' Cambridge University Press, 2010.</ref>
 === 案例 ===
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 * Incentives to exit formal gatherings退出正式聚会的动机
 * Lifestyle trends: What clothing to wear, hairstyle to adopt, and what part of the body to pierce.生活方式:穿什么衣服，留什么发型，穿什么部位。
-* The adoption of political hashtags on Twitter. 在推特上使用政治标签。
+* The adoption of political hashtags on Twitter. 在推特上使用政治标签。<ref>''Differences in the mechanics of information diffusion across topics | Proceedings of the 20th international conference on World wide web''. ''dl.acm.org''. WWW '11. 28 March 2011. pp. 695–704. doi:10.1145/1963405.1963503. ISBN <bdi>9781450306324</bdi>. S2CID 207186115. Retrieved 2020-08-11.</ref>
 === 简单传播的案例 ===
 * 疾病的传播
 * 信息的传播
-== References ==
+== 参考文献 ==
+<references />

@@ 第34行： / 第34行： @@
 == 网络中的扩散和级联行为 ==
 === 参见:全局级联模型 ===
 考虑任意大小的图，节点v的邻居可以分成两个集合：集合A包含v中采用新行为的邻居，集合B是行为保守的邻居的集合。只有当至少''q'' 个邻居遵循行为A时，节点v才会采用A中邻居的行为。

@@ 第1行： / 第1行： @@
 Complex contagion is the phenomenon in social networks in which multiple sources of exposure to an innovation are required before an individual adopts the change of behavior. Centola and Macy show that the weak ties and small worlds networks are both very good for spreading simple contagions.  However, for complex contagions, weak ties and small worlds can slow diffusion.
-复杂传染是社会网络中的一种现象，在这种社会网络中，一个人在采取行为改变之前，需要有多个接触创新的渠道。和 Macy 指出弱关系和小世界网络都非常有利于传播简单传染。然而，对于复杂的传染，弱关系和小世界可以减缓扩散。
+'''复杂传染 Complex contagion'''是[[社会网络]]中的一种现象，在社会网络中，一个人或一个个体需要受到多种不同的影响，会对自身行为采取改变。<ref name="CENTOLA" /> 不同于疾病的简单传染，邻居单一的一次接触并不会使其成功传染，复杂传染的传播需要人群网络中复杂交错的社会和经济因素。例如，一个人有多少个朋友接受这个新想法，以及这些朋友能不能影响这个人，以及他们自己接受改变的倾向。Centola和Macy表明，弱联系和小世界网络都非常有利于简单传播的传染病。然而，对于复杂传染，弱联系和小世界可以有效减缓扩散。
+==传播机制==
-Centola and Macy suggest four mechanisms of complex contagion. These properties explain the need for multiple exposures in the spread of contagion:
+These properties explain the need for multiple exposures in the spread of contagion:
-森托拉和梅西提出了四种复杂传染机制。这些特性解释了在传染蔓延过程中需要多重风险敞口的原因:
+Centola和Macy梅西提出了四种复杂传染的机制。这些性质解释了在传染过程中为什么需要多重曝光：
 '''''Complex Contagion and the Weakness of Long Ties''''' by [[Damon Centola]] of [[University of Pennsylvania]] and [[Michael Macy]] of [[Cornell University]] found that information and disease spread as “simple contagions”, requiring only one contact for transmission, while behaviors typically spread as “complex contagions”, requiring multiples sources of reinforcement to induce adoption.  Centola’s work builds on [[Mark Granovetter|Granovetter’s work]] on the strength of weak ties and threshold models of collective behavior, as well as [[Duncan J. Watts|Duncan Watts]] and [[Steven Strogatz|Steve Strogatz’s]] work on small world networks.<ref>http://science.sciencemag.org/content/329/5996/1194</ref> Centola and Macy show that the weak ties and small worlds networks are both very good for spreading simple contagions.  However, for complex contagions, weak ties and small worlds can slow diffusion.