Graphs and complex networks

图和复杂网络

• 批准号: 227384-2010
• 负责人: Bonato, Anthony
• 金额: $ 1.46万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546734
• 关键词: Graphs; complex; networks

The last decade has witnessed a great deal of research on modelling real-world complex networks such as the link structure of the web. Graphs, consisting of vertices and edges between them, make natural models which simulate and predict properties of complex networks. My goals are to devise and analyze new realistic models for complex networks, using tools from geometry, infinite graphs, and probability theory. On-line social networks (where nodes represent people and edges correspond to social interactions) such as Facebook and Twitter, are an important type of complex network. Such networks contain many dense subgraphs representing tight-knit communities. I will propose new evolutionary models for on-line social networks, as an approach to classifying their community structure. Applications of graph models may lead, in the case of the web, to more efficient search engines; in on-line social networks, they may help map the spread of social influence.

在过去的十年里，人们对现实世界复杂网络的建模进行了大量的研究，比如网络的链接结构。图由顶点和它们之间的边组成，是模拟和预测复杂网络属性的自然模型。我的目标是设计和分析复杂网络的新现实模型，使用几何，无限图和概率论的工具。像Facebook和Twitter这样的在线社交网络（节点代表人，边缘对应社交互动）是一种重要的复杂网络类型。这样的网络包含许多密集的子图，表示紧密结合的社区。我将为在线社交网络提出新的进化模型，作为对其社区结构进行分类的方法。以网络为例，图形模型的应用可能会带来更高效的搜索引擎；在在线社交网络中，它们可能有助于绘制社会影响力的传播图。