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Multi-scale Dynamical Community Detection for the Digital Economy: from analyzing to influencing policy through Open Government data

数字经济的多尺度动态社区检测：从通过开放政府数据分析到影响政策

基本信息

批准号：
EP/I017267/1
负责人：
Sophia Yaliraki
金额：
$ 92.13万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI017267%2F1
关键词：
Multi scale Dynamical Community Detection

项目摘要

The digital age has brought with it an unprecedented gathering of detailed, real-time data from our daily lives, from mobile phone usage to specialized hospital sensors. The availability of such real-world data from a wealth of physical and digital infrastructures coupled with increased computational power offers a unique opportunity to interrogate social behaviour from the level of the individual to the emergence of group dynamics and traits at different levels. Recently, governmental initiatives (specifically in the US and the UK) have been designed to make such datasets available to the wider public. These initiatives offer the possibility to examine quantitatively the influence and effectiveness of policies on different aspects of social dynamics, as well as providing a route for the exercise of citizen participation and feedback. This could lead to improved quality of life in healthcare, traffic, security, or to the design of policies for public spending and usage of resources from the individual level to the collective of groups. These tantalising possibilities have led in the last year to a series of manifesto and even the declaration of the need for a new field, Computational Social Science.. Although those contributions have arisen from different disciplines, they share the belief that the lack of mathematical tools at present for the analysis of such datasets constitutes the fundamental challenge so that the promise of the integration of multi-modal, dynamic datasets can translate into real interpretative results. In particular, there is a need to go beyond the purely (static) statistical methods and to overcome the lack of mathematical, and eventually computational, methodologies that can formalise, interrogate and analyse the data such that hypotheses can be tested and conclusions can be drawn in a rigorous data-driven manner. This proposal, however, goes beyond issues of accessibility and presentation of data and focuses on the development of mathematical tools for the analysis of data in two steps: (1) finding a faithful representation of the data in terms of multi-label, possibly dynamic, networks, and (2) the generation of simplified, intelligible reductions of such networks in terms of a multi-level dynamical hierarchy of communities that can uncover patterns of interaction in the data. The aim of this proposal is to develop robust methodologies for the analysis of networks derived from large, complex social datasets currently made available to the public through the Open Government initiative. Our mathematical tools will address the creation of representative networks from the data and the multi-scale and multi-label analysis of such networks leading to reduced descriptions in terms of dynamical community structures derived from the data without any a priori specification. The datasets chosen will be of current social interest but also exemplify three fundamental characteristics of social datasets that are linked to specific mathematical challenges for their analysis: (i) the multi-scale nature of social networks; (ii) the multi-label characterisation of social datasets; and (iii) the importance of dynamics and flows in social descriptions. The mathematical tools will be specifically applied to the following three areas of high interest for the Digital Economy: Neighbourhood statistics data, the redistricting problem and the recently released budget expenditure data.

数字时代带来了前所未有的详细实时数据收集，这些数据来自我们的日常生活，从手机的使用到专门的医院传感器。来自丰富的物理和数字基础设施的此类现实世界数据的可用性，加上计算能力的增强，为探究从个人层面到不同层面的群体动态和特征的出现的社会行为提供了独特的机会。最近，政府举措（特别是在美国和英国）旨在向更广泛的公众提供此类数据集。这些举措提供了定量研究政策对社会动态不同方面的影响和有效性的可能性，并为公民参与和反馈提供了途径。这可能会改善医疗保健、交通、安全方面的生活质量，或导致从个人层面到集体层面的公共支出和资源使用政策的设计。这些诱人的可能性在去年引发了一系列宣言，甚至宣布需要一个新领域，即计算社会科学。尽管这些贡献来自不同学科，但他们都相信，目前缺乏用于分析此类数据集的数学工具构成了根本挑战，因此多模式、动态数据集集成的承诺可以转化为真正的解释结果。特别是，需要超越纯粹的（静态）统计方法，并克服数学和最终计算方法的缺乏，这些方法可以形式化、询问和分析数据，以便可以以严格的数据驱动方式测试假设并得出结论。然而，该提案超越了数据的可访问性和表示的问题，重点是开发用于分两步进行数据分析的数学工具：（1）在多标签、可能是动态的网络方面找到数据的忠实表示，以及（2）根据社区的多级动态层次结构生成此类网络的简化、可理解的简化，从而可以揭示数据中的交互模式。该提案的目的是开发强大的方法来分析源自目前通过开放政府倡议向公众提供的大型复杂社会数据集的网络。我们的数学工具将解决从数据创建代表性网络以及对此类网络进行多尺度和多标签分析的问题，从而减少从数据派生的动态群落结构的描述，而无需任何先验规范。所选择的数据集将引起当前的社会兴趣，但也体现了社会数据集的三个基本特征，这些特征与分析的特定数学挑战相关：（i）社交网络的多尺度性质； (ii) 社会数据集的多标签表征； (iii) 社会描述中动态和流动的重要性。这些数学工具将专门应用于数字经济备受关注的以下三个领域：社区统计数据、重新划分问题和最近发布的预算支出数据。