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: 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=635124
• 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.

在大数据时代，数据被认为是新的化石燃料。每一次人类与技术的互动，或传感器网络，都会产生新的数据点，这些数据点可以被看作是一个自组织的网络，基于互动的类型。在这些网络（例如Facebook）中，节点不仅包含一些有用的信息（如用户的个人资料、照片、标签），而且还在内部连接到其他节点（基于友谊的关系、相似的用户行为、年龄、地理位置）。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而动态发展。因此，随机几何图是很自然的，很适合建模。理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，对于包括信息和社会科学、经济学、生物学和神经科学在内的广泛领域至关重要。