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[[File:Moreno Sociogram 1st Grade.png|thumb|Moreno's sociogram of a 1st grade class.]]In the 1930s [[Jacob Moreno]], a psychologist in the [[Gestalt psychology|Gestalt]] tradition, arrived in the United States. He developed the [[sociogram]] and presented it to the public in April 1933 at a convention of medical scholars. Moreno claimed that "before the advent of sociometry no one knew what the interpersonal structure of a group 'precisely' looked like" (Moreno, 1953). The sociogram was a representation of the social structure of a group of elementary school students. The boys were friends of boys and the girls were friends of girls with the exception of one boy who said he liked a single girl. The feeling was not reciprocated. This network representation of social structure was found so intriguing that it was printed in [[The New York Times]] (April 3, 1933, page 17). The sociogram has found many applications and has grown into the field of [[social network analysis]].

[[File:Moreno Sociogram 1st Grade.png|thumb|Moreno's sociogram of a 1st grade class.]]In the 1930s [[Jacob Moreno]], a psychologist in the [[Gestalt psychology|Gestalt]] tradition, arrived in the United States. He developed the [[sociogram]] and presented it to the public in April 1933 at a convention of medical scholars. Moreno claimed that "before the advent of sociometry no one knew what the interpersonal structure of a group 'precisely' looked like" (Moreno, 1953). The sociogram was a representation of the social structure of a group of elementary school students. The boys were friends of boys and the girls were friends of girls with the exception of one boy who said he liked a single girl. The feeling was not reciprocated. This network representation of social structure was found so intriguing that it was printed in [[The New York Times]] (April 3, 1933, page 17). The sociogram has found many applications and has grown into the field of [[social network analysis]].

20世纪30年代，格式塔传统的心理学家雅各布·莫雷诺Jacob Moreno来到美国。 在1933年4月的一次医学学者大会上，他在大会上展示了他制作的[[社网图]] sociogram。 莫雷诺声称“在社会计量学出现之前，没有人知道一个群体的人际关系结构‘精确地’看起来像什么”(莫雷诺，1953)。 这张社网图展示一群小学生的社会结构。男孩是男孩的朋友，女孩是女孩的朋友，只有一个男孩说他喜欢另一个单身女孩但是没有得到回应。 这个网络展示的社会结构非常有趣，也被刊登在《纽约时报》上(1933年4月3日，第17页)。 社网图已经有许多的应用场景，并且已经发展成为[[社会网络分析]]social network analysis的一个子领域。

20世纪30年代，格式塔传统的心理学家雅各布·莫雷诺（Jacob Moreno）来到美国。他提出了[[社会人际关系图]] （sociogram）这一概念，并在1933年4月的一次医学学者大会上向公众展示了他制作的[[社会人际关系图]]。莫雷诺声称，“在社会计量学出现之前，没有人知道一个群体的人际关系结构‘精确的’样子”(莫雷诺，1953)。这张社会图展示了一组小学生的社会结构。男孩是男孩的朋友，女孩是女孩的朋友，只有一个男孩说他喜欢另一个单身女孩但是没有得到回应。这个网络展示的社会结构非常有趣，也被刊登在《纽约时报》上（1933年4月3日，第17页）。社会图已经有许多的应用场景，并且已经发展成为[[社会网络分析]]（social network analysis）的一个子领域。

Probabilistic theory in network science developed as an offshoot of [[graph theory]] with [[Paul Erdős]] and [[Alfréd Rényi]]'s eight famous papers on [[random graphs]]. For [[social networks]] the [[exponential random graph model]] or p* is a notational framework used to represent the probability space of a tie occurring in a [[social network]]. An alternate approach to network probability structures is the [[network probability matrix]], which models the probability of edges occurring in a network, based on the historic presence or absence of the edge in a sample of networks.

Probabilistic theory in network science developed as an offshoot of [[graph theory]] with [[Paul Erdős]] and [[Alfréd Rényi]]'s eight famous papers on [[random graphs]]. For [[social networks]] the [[exponential random graph model]] or p* is a notational framework used to represent the probability space of a tie occurring in a [[social network]]. An alternate approach to network probability structures is the [[network probability matrix]], which models the probability of edges occurring in a network, based on the historic presence or absence of the edge in a sample of networks.

20世纪90年代，在Paul Erdős和Alfréd Rényi发表了8篇关于随机图的著名论文之后，网络科学中的概率论 Probabilistic theory 作为图论的一个分支发展起来了。 对于[[社交网络]]social network来说，指数随机图模型或p* (是一个记号框架），用来表示在一个社交网络中连边在概率空间发生的概率。 网络概率结构的另一种替代表示方法是网络概率矩阵，它根据网络样本中边的历史信息来计算这条边在网络中出现的概率。

20世纪90年代，在Paul Erdős和Alfréd Rényi发表了8篇关于随机图的著名论文之后，网络科学中的概率论（Probabilistic theory）作为图论的一个分支发展起来了。对于[[社交网络]]（social network）来说，指数随机图模型或p*是一个记号框架，用于表示社交网络中发生关系的概率空间。网络概率结构的另一种替代表示方法是网络概率矩阵，它根据网络样本中边的历史信息来计算这条边在网络中出现的概率。

In 1998, [[David Krackhardt]] and [[Kathleen Carley]] introduced the idea of a meta-network with the PCANS Model. They suggest that "all organizations are structured along these three domains, Individuals, Tasks, and Resources". Their paper introduced the concept that networks occur across multiple domains and that they are interrelated. This field has grown into another sub-discipline of network science called [[dynamic network analysis]].

In 1998, [[David Krackhardt]] and [[Kathleen Carley]] introduced the idea of a meta-network with the PCANS Model. They suggest that "all organizations are structured along these three domains, Individuals, Tasks, and Resources". Their paper introduced the concept that networks occur across multiple domains and that they are interrelated. This field has grown into another sub-discipline of network science called [[dynamic network analysis]].

1998年，David Krackhardt和Kathleen Carley提出了用PCANS模型构建元网络meta-network的想法。 他们建议所有的组织都通过“个人、任务和资源”来构建。 他们的论文介绍了新的网络概念，即网络发生在多个领域的且相互关联。 这个领域已经发展也成为网络科学的另一个子学科，叫做[[动态网络分析]]dynamic network analysis。

1998年，David Krackhardt和Kathleen Carley提出了用PCANS模型构建元网络（meta-network）的想法。他们认为，“所有的组织都是按照‘个人、任务和资源’这三个领域来构建的”。他们的论文介绍了新的网络概念，即网络发生在多个领域且相互关联。这个领域也已经发展成为网络科学的另一个子学科，即[[动态网络分析]]（dynamic network analysis）。

More recently other network science efforts have focused on mathematically describing different network topologies. Duncan Watts reconciled empirical data on networks with mathematical representation, describing the [[small-world network]]. [[Albert-László Barabási]] and [[Reka Albert]] developed the [[scale-free network]] which is a loosely defined network topology that contains hub vertices with many connections, that grow in a way to maintain a constant ratio in the number of the connections versus all other nodes.  Although many networks, such as the internet, appear to maintain this aspect, other networks have long tailed distributions of nodes that only approximate scale free ratios.

More recently other network science efforts have focused on mathematically describing different network topologies. Duncan Watts reconciled empirical data on networks with mathematical representation, describing the [[small-world network]]. [[Albert-László Barabási]] and [[Reka Albert]] developed the [[scale-free network]] which is a loosely defined network topology that contains hub vertices with many connections, that grow in a way to maintain a constant ratio in the number of the connections versus all other nodes.  Although many networks, such as the internet, appear to maintain this aspect, other networks have long tailed distributions of nodes that only approximate scale free ratios.

最近其他网络科学的研究致力于用数学的方式描述不同网络的拓扑结构。[[邓肯·瓦茨]]Duncan Watts 将网络上的经验数据与数学表达相结合，描述了小世界网络。 [[艾伯特-拉斯洛·巴拉巴西 Albert-László Barabási]] 和 [[Reka Albert]]发现了无标度网络，简单说就是包含Hub中心节点（连边数量很多的节点），且连边的数量和节点数量呈现常数比率的增长方式，即一个网络上节点的度（节点所在连边的数量）分布服从幂指数为2和3之间的幂律分布。尽管许多网络，比如互联网，似乎保持了这样的特性，但是其他网络的节点分布表现出长尾特性，仅仅是接近无标度比例。

最近，其他网络科学的研究致力于用数学的方式描述不同网络的拓扑结构。[[邓肯·瓦茨]]（Duncan Watts）将网络的经验数据与数学表达相结合，描述了小世界网络。 [[艾伯特-拉斯洛·巴拉巴西（Albert-László Barabási）]] 和 [[Reka Albert]]提出了无标度网络，这是一种定义不明确的网络拓扑结构，简单说就是包含Hub中心节点（连边数量很多的节点），且连边的数量和节点数量呈现常数比率的增长方式。尽管许多网络，比如互联网，似乎保持了这样的特性，但是其他网络的节点分布表现出长尾特性，仅仅是接近无标度比例。