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，数据被认为是新的化石燃料。每个人机交互或传感器网络都会生成新的数据点，根据交互类型，这些数据点可以被视为自组织网络。在这些网络（例如，Facebook）中，节点不仅包含一些有用的信息（例如用户的个人资料、照片、标签），而且还内部连接到其他节点（基于友谊、相似用户行为、年龄、地理位置的关系）。这样的网络是大规模的、自组织的、分散的，并且随着时间的推移动态发展。因此，随机几何图变得很自然并且非常适合对其进行建模。了解驱动复杂网络的组织和行为的原理以及基于这些网络的算法对于信息和社会科学、经济学、生物学和神经科学等广泛领域至关重要。 PI 的研究计划通过专注于复杂网络的建模和挖掘（纯研究以及与行业合作伙伴的应用研究）来促进我们的理解。该项目本质上是多学科的，因此需要至少三个领域的知识、技能和工具的相当独特的融合：1) 数学和理论计算机科学，2) 社会科学，3) 应用计算机科学。这三个领域日益相互关联，需要在所有这些领域积累经验才能在该领域产生重要影响。