Social Networks Analysis

社交网络分析

• 批准号: RGPIN-2015-05769
• 负责人: Marbach, Peter
• 金额: $ 2.11万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658717
• 关键词: Social; Networks; Analysis

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.**

在线社交网络已经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程序包括Facebook，LinkedIn和Twitter，支持数百万活跃用户。虽然非常受欢迎，但我们相信这些应用程序只是触及了可能做的事情的表面，并且在开发新的和更先进的在线社交网络应用程序方面存在巨大的机会。开发这样的应用程序和算法提出了新的和迷人的研究问题。主要的研究挑战是发展一个正式的了解在线社交网络，无论是在这些网络是如何形成的，并随着时间的推移，以及它们如何可以用来有效地共享和分发信息。拟议的研究的目标是解决这些挑战的社交网络的理论基础。因此，拟议的研究计划的重点是创建数学模型，以了解社交网络是如何形成的，并用于共享和分发信息。我们相信，正式了解社交网络是如何形成的，并用于共享和分发信息，可以导致从根本上新的算法，无论是社会网络分析和社会网络应用程序的实施。特别是，社交网络的显著特征是网络拓扑结构与网络内的信息流之间存在强耦合。我们相信，通过对这种关系进行定量建模和分析，有可能设计出比现有算法更简单、更高效的算法，同时（接近）最佳性能。拟议的研究有可能对学术研究界和实际的社交网络应用产生影响。一方面，建立一个可以用来推导社会网络性质的数学模型，并推导出社会网络分析算法，将加深对社交网络的理解。另一方面，这种理解也可以用于创建和设计新颖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。特别是，我们设想新的社交网络应用程序，能够更自然地进行交互，并能够更准确地过滤用户感兴趣的内容。这与今天的社交网络应用程序（如Twitter）中存在的大量噪音形成鲜明对比。