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清华大学社会网络研究中心介绍

2016-5-14 20:28| 发布者: admin| 查看: 1865| 评论: 0

摘要: 2013年1月3日经清华大学2012-2-13学年第12次校务会议讨论通过,决定成立清华大学社会网络研究中心,简称社会网络研究中心。英文名称Center for Social Network Research,Tsinghua University,英文缩写CSNR。中心为跨 ...
      清华大学社会网络研究中心(Center for Social Network Research,Tsinghua University,英文缩写CSNR),成立于 2013 1 3日。作为清华大学研究中心,其目的是建立平台去推进物理学、计算机科学、和社会科学研究者之间的合作,这样的合作有利于他们探索跨学科方法,研究复杂社会网络中的大数据集。

大数据为跨学科研究带来了许多机遇和挑战:各方面交叉的理论知识得到开展,并且在社会网络的结构、 动态的过程和后果方面,积累了大量的假设、模型和实证研究结果。为了有效整合数据挖掘、理论建构与动态复杂系统的预测,我们首先需要创建一个以数据挖掘为出发点的方法论框架。以在线大数据为挖掘对象,可以发现新社会现象,然后通过各种定性和调查研究,解释这些发现,这将进一步帮助我们揭示社会实际情况,并验证理论假说。最后,基于被验证的理论可以建立包括人类行动和网络结构的共同演化模型。跨学科的方法利用在此坚实的理论基础上建立的动态网络模型,从而预测社会系统非线性演化的轨迹。研究在数据挖掘、理论建构与动态复杂系统的预测一轮又一轮进行,使我们能对非线性发展的社会系统中涌现的新生社会现象有所解释与预测。

CSNR 的目标是推进一系列项目,用以培养不同研究背景的学生,让他们不同的专业领域在方法上和实质上交流、整合并相互印证发明。依靠这样的跨学科培训学生以及联合研究的方法,我们可以期待大数据领域的新发现以及社会网络研究方面的大进展。

 

Introduction to Tsinghua Center for Social Network Research

 (in brief, CSNR)

 

The Tsinghua Center for Social Network Research was founded on Jan. 1st 2013. As a Tsinghua University research center, its purpose is to develop a platform for cooperation among physicists, computer scientists, and social, behavioral and epidemiology researchers, so that they can explore a set of interdisciplinary methodological approaches for studying complex social networks in big data.

Big data brings a lot of opportunities and challenges for interdisciplinary research: Various domains of knowledge have developed and accumulated a large body of hypotheses, models and empirical findings on the structure, dynamic processes, and consequences of social networks.

In order to arrive at a consensus we first need to create a methodological framework that takes data mining as its starting point. The object of research is online big data, which has the untapped potential to yield findings about new social phenomena. The next step is interpreting these findings through various qualitative and survey studies, which will further help us reveal grounded truth to verify theoretical hypotheses. The final step is building a model that encompasses the co-evolution of human actions and network structure based on the operative theories. With a model built on this solid foundation, researchers trained with interdisciplinary methods have the required information to predict new facts from their results.

Running in parallel to our research are real-world surveys that often generate new facts that conflict with our interpretation of the results of data mining. In this kind of scenario, we must return to our data and begin an iterative process of data mining, theory development and dynamic model building; until we can reconcile the two sets of results.

The goal of CSNR is to develop a series of programs for training students with different research backgrounds, and mesh their different areas of expertise into a methodologically and substantively integrated whole. From this interdisciplinary training method we can expect new results and considerable progress in social network research.


 


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