Hidden features and information diffusion in large social networks

大型社交网络中的隐藏特征和信息传播

• 批准号: RGPIN-2017-05112
• 负责人: Janssen, Jeannette
• 金额: $ 2.48万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634938
• 关键词: Hidden; features; information; diffusion; large

In social networks, connections are made based on a degree of communality between the users. Typically, links are more likely if users share common features or belong to the same community. Network formation can be modelled by assuming the nodes to be embedded in a feature space, and links to be formed through a stochastic process which depends on the distance between the nodes in the underlying space. The aim of the proposed research is to develop models and algorithms that will allow for the extraction of the latent feature space from the link structure of large networks. I will use theoretical tools to study the relationship between spatial features of the nodes and the link structure, using the infinite limit to understand the behaviour as the network gets large. I will also further develop and analyze a spatial model for growing networks, built on the principle of preferential attachment, which leads to graphs that exhibit commonly observed features such as a power law degree distribution, high clustering, and the presence of long links that lead to the small world property.

在社交网络中，用户之间的联系是建立在一定程度的社区基础上的。通常，如果用户共享共同的功能或属于同一个社区，则链接更有可能出现。网络形成可以通过假设节点嵌入在特征空间中来建模，并且通过一个随机过程来形成链接，该过程取决于底层空间中节点之间的距离。提出的研究目的是开发模型和算法，允许从大型网络的链接结构中提取潜在特征空间。我将使用理论工具来研究节点的空间特征与链路结构之间的关系，使用无限极限来理解网络变大时的行为。我还将进一步开发和分析基于优先依恋原则的增长网络的空间模型，该模型导致图形显示出常见的特征，如幂律度分布、高聚类和导致小世界属性的长链接的存在。