Modelling and Mining Complex Networks

复杂网络的建模和挖掘

• 批准号: RGPIN-2017-04402
• 负责人: Pralat, Pawel
• 金额: $ 1.68万
• 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: 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=662565
• 关键词: Modelling; Mining; Complex; Networks

In the big data era, data is considered as the new fossil fuel. Every human-technology interaction, or sensor network, generates new data points that can be viewed, based on the type of interaction, as a self-organizing network. In these networks (for example, Facebook) nodes not only contain some useful information (such as user's profile, photos, tags) but are also internally connected to other nodes (relations based on friendship, similar users behaviour, age, geographic location). Such networks are large-scale, self-organizing, decentralized, and evolve dynamically over time. As a result, random geometric graphs turn out to be natural and well suited in modelling them. Understanding the principles driving the organization and behaviour of complex networks, as well as algorithms based on these networks, is crucial for a broad range of fields, including information and social sciences, economics, biology, and neuroscience.******The research program of the PI contributes to our understanding by focusing on modelling and mining of complex networks (pure research as well as applied one with industry partners). The program is multidisciplinary in nature and as such requires a rather unique blend of knowledge, skills, and tools from at least three areas: 1) mathematics and theoretical computer science, 2) social science, and 3) applied computer science. Increasingly, all three areas are interconnected, and experience in all of them is required in order to make an important impact in the field.

在大数据时代，数据被视为新的化石燃料。每个人类技术互动或传感器网络都会根据相互作用的类型作为自组织网络生成可以查看的新数据点。在这些网络（例如，Facebook）中，节点不仅包含一些有用的信息（例如用户的配置文件，照片，标签），而且内部连接到其他节点（基于友谊，相似的用户行为，年龄，地理位置位置）。此类网络是大规模，自组织，分散的和随着时间的流逝而动态发展的。结果，随机的几何图形被证明是自然的，并且非常适合对它们进行建模。了解推动复杂网络的组织和行为以及基于这些网络的算法的原则，对于广泛的领域至关重要，包括信息和社会科学，经济学，生物学和神经科学。该计划本质上是多学科的，因此需要至少三个领域的知识，技能和工具的融合：1）数学和理论计算机科学，2）社会科学，以及3）应用计算机科学。越来越多的三个领域都是相互联系的，为了对现场产生重要影响，所有三个领域都需要在所有三个领域中进行互连。