Social Networks Analysis

在线社交网络彻底改变了我们在互联网上互动和分享信息的方式。流行的社交网络应用包括Facebook、LinkedIn和Twitter，它们支持数百万活跃用户。虽然非常受欢迎，但我们认为，这些应用程序只是触及了可能做的事情的表面，开发新的和更高级的在线社交网络应用程序有巨大的机会。 开发这样的应用程序和算法带来了新的、引人入胜的研究问题。主要的研究挑战是建立对在线社交网络的正式理解，包括这些网络是如何形成和随着时间的推移而演变的，以及如何利用它们来有效地共享和分发信息。这项拟议研究的目标是通过努力建立社交网络的理论基础来应对这些挑战。因此，拟议的研究计划的重点是创建数学模型，说明社交网络是如何形成的，以及如何利用社交网络来共享和分发信息。 我们相信，正式了解社交网络是如何形成并用于共享和分发信息的，可以从根本上产生用于社交网络分析和社交网络应用程序实施的新算法。特别是，社交网络的显著特征是在网络拓扑和网络内部的信息流之间存在着强耦合。我们相信，通过定量建模和分析这种关系，可以设计出比现有算法更简单、更高效的算法，但又能导致(接近)最优性能。 这项拟议的研究可能会对学术研究界和实际的社交网络应用产生影响。一方面，建立可用于推导社会网络性质的数学模型和推导社会网络分析算法将加深对社会网络的理解。另一方面，这种理解也可以用来创建和设计新的在线社交网络应用程序，这些应用程序远远超出了当今社交网络应用程序的能力范围。特别是，我们设想新的社交网络应用程序能够更自然地交互，并能够更准确地过滤用户感兴趣的内容。这与如今Twitter等社交网络应用程序中存在的大量噪音形成了鲜明对比。

Online social networks have revolutionized the way we interact and share information over the Internet. Popular social networking applications include Facebook, LinkedIn and Twitter, which support millions of active users. While being enormously popular, we believe hat that these applications only scratch the surface of what is possible to do and that there are tremendous opportunities in developing new and more advanced online social networking applications. Developing such applications and algorithms poses new and fascinating research problems. The major research challenge is to develop a formal understanding of online social networks both in terms of how these networks are formed and evolve over time, as well as how they can be used to efficiently share and distribute information. The goal of the proposed research is to address these challenges by working towards a theoretical foundation of social networks. As such, the focus of the proposed research program is on creating mathematical models for how social networks are being formed and used to shared and distribute information. We believe that a formal understanding of how social networks are being formed and used to share and distribute information can lead to fundamentally new algorithms both for social networking analysis and the implementation of social networking applications. In particular, the striking feature of social networks is that there exists a strong coupling between the network topology and the information flow within the network. We believe that by quantitatively modeling and analyzing this relation, it is possible to design algorithms that are simpler and are more efficient compared with existing algorithms, and yet lead to (close) to optimal performance. The proposed research has the potential to have an impact both on the academic research community and for practical social networking applications. On one hand, creating a mathematical models that can be used derive properties of social network and to derive social network analysis algorithms will deepen the understanding of social networks. On the other hand, this understanding can also be used to create and design novel online social networking applications that go well beyond of what today's social networking applications are capable of doing. In particular, we envision new social networking application that are able to interact with more naturally, and are able to more accurately filter the content that users are interested in. This is in contrast with the large amount of noise that exists in today's social network application such as Twitter.