New approaches to the random generation of combinatorial objects

随机生成组合对象的新方法

• 批准号: DP160100835
• 负责人: Prof Nicholas Wormald
• 金额: $ 28.3万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-04-04 至 2020-04-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160100835
• 关键词: New; approaches; random; generation; combinatorial

This project intends to create new methods to generate networks. Networks are useful theoretical combinatorial objects, which also occur in natural contexts such as social networks or the world wide web. Some studies require network simulations and often the best option for this is to generate the networks uniformly at random. Then each one of a given size occurs with the same probability. This project plans to introduce new approaches to performing this task, enabling it to be done efficiently for a much broader range of networks, graphs and other combinatorial objects than before. This would permit better simulations and efficient and truthful sampling, giving better prediction of network properties and better analysis of algorithms applied to networks.

该项目旨在创建生成网络的新方法。网络是有用的理论组合对象，其也出现在自然环境中，例如社交网络或万维网。一些研究需要网络模拟，通常最好的选择是随机均匀地生成网络。那么每一个给定大小的事件都以相同的概率出现。该项目计划引入新的方法来执行这项任务，使其能够有效地完成比以前更广泛的网络，图形和其他组合对象。这将允许更好的模拟和有效和真实的采样，提供更好的预测网络属性和更好的分析应用于网络的算法。