Networks 101

Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.

A trio of researchers at the University of Southern California has uncovered a social-network illusion that might explain why some things become popular in cyberspace while others do not. Kristina Lerman, Xiaoran Yan and Xin-Zeng Wu have written a paper describing the illusion and how it works and have ...

and What It Means for Business, Science, and Everyday Life

In recent years there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.

Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

The book is based on an inter-disciplinary course that we teach at Cornell. The book, like the course, is designed at the introductory undergraduate level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with optional advanced sections.

The book is published by Cambridge University Press (2010); for more information, please see Cambridge's page for the book.

Pentland and his teams have found that they can study patterns of information exchange in a social network without any knowledge of the actual content of the information and predict with stunning accuracy how productive and effective that network is, whether it’s a business or an entire city. We can maximize a group’s collective intelligence to improve performance and use social incentives to create new organizations and guide them through disruptive change in a way that maximizes the good. At every level of interaction, from small groups to large cities, social networks can be tuned to increase exploration and engagement, thus vastly improving idea flow.

Social Physics is a new way of understanding human behavior based on analysis of Big Data.

The contributors to the Social Physics are a set of researchers who are connected through their association with the Human Dynamics Lab at MIT.

Explore the critical questions posed by how the social, economic, and technological realms of the modern world interconnect.

学科交叉是当代科学技术与经济社会发展的一个重要特征。理工科的学生了解一些社会科学基本原理，社会科学的学生了解如何用理工科的方法与工具来分析社会科学问题，对于开阔学生视野，更好地面对未来的挑战，将会是有益的。
本课程从交叉学科的视角出发，综合运用经济学、社会学、计算与信息科学，以及应用数学的有关概念与方法，考察网络行为原理及其效应。所涉及的内容展示了在网络的作用下正在浮现与发展起来的一些交叉学科领域，讨论了社会、经济和技术领域相互联系的若干基本问题。课程的内容范畴比较宽，但不会很深，旨在引导学生体会跨学科的思想，综合运用多学科的方法，抽象、分析与推理现实经济社会生活中的若干典型问题。

MIT Connection Science is improving organizations through deep insights into human behavior and targeted interventions that leverage human networks. With applications ranging from energy to financial services to social adoption of new ideas, we design better tools to foster a better society.

The Silicon Valley Networks Analysis Project at Stanford University is dedicated to researching the social networks among firms in Silicon Valley. Chaired by Professor Mark Granovetter of the Department of Sociology, the project currently administers a number of studies primarily related to the social network dynamics of venture capital firms.
Splash Image Though everyone agrees that the most crucial aspect of Silicon Valley's dramatic success is its networks, there has been virtually no systematic study of their history, structure and functioning. This project attempts to map these networks and their evolution over time. Using relational database methods developed in the study of the electricity industry, the plan is to track the affiliation of company principals, trace the "genealogies" of new firms, and show how movements of people among firms continuously shapes and reshapes networks of both individuals and organizations. The research group also intends to study the institutional complex that supports local industrial activity, including financial, educational, legal, and political sectors. The end result should be the first comprehensive sociological account of an "industrial district".

The project team is comprised of a collection of doctoral candidates and interested undergraduates. If you would like more information on how to get involved with the project, please visit the contact page for more information.

barabasilab.com -- center for complex network research. Our research, directed by Professor Albert-laszlo Barabasi, has a simple objective: think networks. It is about how networks emerge, what they look like, and how they evolve; and how networks impact on understanding of complex systems.

We are pleased to introduce IBM's Center for Resiliency and Sustainability or CRS (the parent of the IBM Network Science Research Center or NSRC), which is an integral part of IBM Safer Planet and Smarter Cities. It is a collaboration among scholars and researchers from IBM, Swansea University, and many other well renowned academic institutions and organizations. The CRS provides access to practitioners and subject matter experts as well as to pertinent information regarding information technology (e.g. IBM WatsonTM, Analytics, Sensemaking, Streams Computing, Cloud, etc.) that can be well applied not only to your mission, but also to that of the society-at-large.

Sparked by the confluence of a rapid rise in network techniques across the social and physical sciences, the Duke Network Analysis Center seeks to crystallize the latent talent in this area at Duke and around the triangle to build a world-premier source for cutting edge network studies. The rise of network science over the last 10 to 15 years is predicated on building scientific insight by modeling the complex patterns of connections that link primary elements to each other. The range of such work is exceedingly broad, since the unifying network abstraction is virtually content free. Thus, social network studies add relational context to our understandings of human behavior in areas as diverse as health, culture, organizations, science or politics. Similar tools are used to great advantage in biology, physics, and ecology to name just a few.

Welcome to the web home of the Yale Institute for Network Science (YINS). Our physical home is at 17 Hillhouse Avenue where we took up residence in January of 2014. Our interdisciplinary institute focuses on diverse problems and solutions in network science, in the social, physical, biological, and engineering sciences. Our weekly interdisciplinary seminar series involves an outstanding lineup of presenters drawn from throughout Yale and around the world. We are pleased to provide information here about the seminar series and other events at YINS; research by YINS faculty, post-docs, and students; classes and other resources at Yale related to network science; and news about our institute and the field of network science.

Network Science is a new journal for a new discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it.

The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this new discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.

社会网络分析思想最早起源于20世纪30年代西方的心理学和人类学研究。

社会网络分析主要沿着下面三个大的方向进行发展：

(1)社会计量学派运用图论方法对社会网络分析做出了杰出的贡献。

社会网络分析方法形成的技术基础是社会计量学（Sociometrics)和图论。

(2)20世纪30年代的哈佛学派致力于研究人际模式和"团伙"形式。

(3)20世纪50年代，曼彻斯特学派进行了大量的社会网络研究，他们把社会网络分析技术运用到了人际关系上。

这些研究早在2003年时，MIT和STADFORD等等北美大学开始相关研究和实践，特别是类似社会网络服务时不少人就借助于软件可视来分析BLOG的相关联接。如Ross Mayfield就在其Blog Tribe 做类似的分析。 那个时期也是北美社会软件蓬蓬勃勃兴起的初期。

目前，在社会网络分析理论，国内尚没有开展深入的研究工作。2004年，国内不少高校邀请海外学者来华讲授“社会网络分析方法”。这也是国内社会科学领域接触社会网络分析开始的一年，开始熟悉以数学语言来描述社会网络；这可能也是自数量经济分析后，社会科学领域又一次与数理科学发生交叉碰撞的被动接受的趋势。这些交流后的文集，包括清华大学社会学邀请了加州大学的罗家德；以及由中国政法大学刑事司法学院社会学教研室主办“社会网络分析方法专题讲座”(“Method of Social Network Analysis”)邀请了来自美国加州大学欧文分校数理行为科学研究所的人类学兼经济学教授钟独安先生(Duran Bell)以及东卡罗莱纳大学人类学系助理教授伊文雅博士(Christine Avenarius)。

社会网络分析是当代西方主流社会学一个重要的研究方向，作为一种研究范式和一个具体的研究领域，社会网络分析的产生与发展是在整个当代科学界重视结构性与系统性的大背景下的产物。

而在社会学学科史上，几乎很难找到另外一个研究领域，像社会网络分析这样，脱胎于社会学，而被其他社会科学和自然科学学科广泛接受，产生出一大批的实证研究，并成为学术界的研究热点。

如果我们的目的是理解人类的行为而不只是记录它，我们就需要了解首属群体、邻里、组织、社交圈、社区，以及互动、沟通、角色期望、社会控制。

Mark Granovetter (born October 20, 1943) is an American sociologist and professor at Stanford University.[1] Granovetter was recently recognized as a Citation Laureate by Thomson Reuters and added to that organization’s list of predicted Nobel Prize winners in economics for the year 2014. Data from the Web of Science show that Granovetter has written both the first and third most cited sociology articles. He is best known for his work in social network theory and in economic sociology, particularly his theory on the spread of information in social networks known as "The Strength of Weak Ties" (1973).

Albert-László Barabási (born March 30, 1967) is a Romanian-born Hungarian-American physicist, best known for his work in the research of network theory.
He is the former Emil T. Hofmann professor at the University of Notre Dame and current Distinguished Professor and Director of Northeastern University's Center for Complex Network Research (CCNR) associate member of the Center of Cancer Systems Biology (CCSB) at the Dana–Farber Cancer Institute, Harvard University, and professor at the Center for Network Science [1] at Central European University.
He introduced in 1999 the concept of scale-free networks and proposed the Barabási–Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the World Wide Web or online communities.

Moreno is also widely credited as one of the founders of the discipline of social network analysis, the branch of sociology that deals with the quantitative evaluation of an individual's role in a group or community by analysis of the network of connections between them and others. His 1934 book Who Shall Survive? contains some of the earliest graphical depictions of social networks (sociograms). In this book, he introduced a famous explanation, why a pandemic of runaways emerged at the New York Training School for Girls in Hudson.

最近这几篇贴子的缘起是Group Social Graph社交图这个小容自己生造的名词。看起来有些滥用概念之嫌。小容当时想到这个名字的确是借用Social Graph这个名词，现在这几篇Tag为群体社交图的贴子，已经脱离Social Graph的原意，转到讨论群体的身份、群体和群体的关系、群体和内部个体的关系，群体的“在线”和“离线”关系，以及“在线群体”和“离线群体”的关系等等。不过，小容会继续使用这个Tag来标记这一组小文章。

另外需要特别声明的是，这几篇贴子里小容所使用的“群体”，并没有特别严格的定义，所以会看到在具体的例子上，会有商业公司、非赢利组织、在线社团等等，关于“群体”的具体类别，以及各类别的差异，将会在以后的贴子里讨论。

小容一直想要推荐两本书给对社会性网络(Social Networks) 感兴趣的朋友看。一本是《硅谷优势：创新与创业精神的栖息地》（The Silicon Valley Edge: a habitat for innovation and entrepreneurship）另一本是《社会资本》(Social Capital:A Theory of Social and Action) ，介绍两个教授给大家认识，一个马克•格兰诺维特（Mark Granovetter），另一个是林南(Nan Lin)。
这个介绍性文章共分为四段,大家可以点击最下面的链接看全文!

·马克•格兰诺维特（Mark Granovetter）和“硅谷社会网络”SiVNAP研究项目组
·马克•格兰诺维特和”弱链接”的故事
• 硅谷的社会性网络
• 中国大陆的社会文化背景

每一个知识工作者都应当将知识探索的过程公开在网络上进行，让知识探索的过程得以社会化，并且以更快的速度进行。对于中文blogosphere来说，只有当越来越多的知识工作者开始写blog，并且坚持写blog的时候，垂直领域的信息才会涌现，中文web的信息价值才会逐渐跃升到知识探索层面。

需要水平过滤器的时代，还需要垂直信息的把关人。

这篇贴子谈的是心理学家和大众需求之间的鸿沟，实际上，这是在各个学科和产业普遍存在的现象。学理论知识是基础，而实践性知识是应用。显然实践需要理论指 导，否则人们就找不到方向，会迷失在纷繁复杂的表象中。而学理论知识也要从实践中吸取养分，否则容易沦落为空中楼阁般的学术游戏。

阳志平：【个人一直觉得，心理学在工业、设计、创意、互联网领域的商业价值远远大于在心理咨询领域的商业价 值。无它，在心理咨询领域，心理学的发展过于依赖专家的个人价值、个人口碑。专家的技能、咨询的效果缺乏一个定量的评价体系，从而很难具备大规模复制的可能性。人因实验室这个项目就是一群志同道合的朋友，在这个领域的探索。

人因实验室是一个由人因领域的专业研究者、爱好者与商业推动者共同维护、参与的一个研究性组织。随着人因实验室的发展壮大，不排除它成为商业组织的可能性。】

前个星期帮朋友在Amazon上买了几本书，Amazon的确认邮件说预计在5月5日送到，没想到今天下午就收到了Amazon寄来的书了。

其中一本书是Forrester咨询公司的两位资深分析师一起写的Groundswell，这本书讨论的是在社会化网络的背景下，消费者和消费者互相之间的联系愈来愈紧密，这种消费者之间的联系力量将改变现有商业的游戏格局，因此，商业公司要去理解这种改变，与时俱进，善用网络，迎合和适应这种力量，而不是触怒和抵触这种力量。

这本书讲了许多故事，同时引用了大量Forrester咨询公司的调查研究数据。Forrester使用一个名字叫做Social Technographics的调研工具，根据用户在社会化网络中的使用习惯，定义了Creator,Criticse,Collectors,Joiners,Spectators,Inactives这六种角色，并定期调查用户的使用习惯，得到这六种角色的统计数据。想要仔细地了解这个工具，请看这里的幻灯片。Forrester也免费提供基于这个工具展开的调查数据，请看这里。

今天在Wealink里看到有人在推荐《社会资本：造就优秀公司的重要元素》这本书。于是写了几句评论，后来也整理了一下，到豆瓣里发了一个简短的书评。内容如下：

社会资本和社交网络分析紧密联系在一起。人们对社会资本的研究范围非常广泛，从理论层面的定义到实务层面的工具。在这么多的议题当中，考察企业内部的社交网络结构以及社会资本，是现在的管理者和人力资源研究者的全新议题。

知识管理专家唐·科恩和劳伦斯·普鲁萨克写的《社会资本：造就优秀公司的重要元素》这本书就是具体探讨这个议题。用书籍的宣传介绍来说：“本书在分析关系 在组织成功中所扮演的关键角色方面是一本里程碑式的著作。它帮助各个层次的员工认识到社会资本具有无限的力量，可以帮助人们工作得更出色，并使公司成为更 吸引人的工作场所。作者令人信服地指出：几乎每一个管理决策都可能成为社会资本增加或者减少的契机——这些决策包括从招聘、解聘到新技术的应用乃至办公室 的设计。他们还揭示了对社会资本进行投资所带来的好处，例如更高的忠诚度、更出色的合作，以及对顾客需求的更明智的回应等。”