Dynamical Network Analysis and Machine Learning for Computational Social Sciences

数字化的推动，以及跨不同领域的数据获取和信息整合的增加，正在加强网络社会的影响。个人数据和行为现在被嵌入到社交网络中，通过在线社交互动实现了巨大的信息流。这些新的社会联系模式导致了不同类型和范围的可用数据的巨大增加，这对社会科学中传统的量化方法提出了挑战。然而，它们也提供了巨大的机会，通过提取和分析社交媒体增强的社会经济数据来获得新的见解。为了实现这一目标，我们需要基于网络上的动态和随机过程的数学和计算技术的发展，以捕捉社会动态的关键特征。这个博士项目将研究用于网络分析的新的数学和算法工具，以应对大数据给网络社会带来的挑战。在方法论上，该项目位于网络分析、图论、动力系统、随机过程和基于图的统计学习的交叉点上。从社会数据集的分析中产生的网络具有独特的属性，这将符合我们的特殊研究重点。具体地说，我们将重点介绍可以处理有向加权图的方法；几何和地理限制的网络；以及具有多尺度和多层组织的图，这些图可以捕捉丰富的社交互动和信息流。作为第一个目标，我们将考虑基于随机游走的多尺度图划分方法(即马尔可夫稳定性分析)中的尺度选择问题。这种动态的图划分方法自然地扩展到有向、加权、多尺度网络，使其高度适用于社交网络。然后，我们将把这种分析推广到多层网络和超图，多层网络允许集成节点之间的不同类型的交互，超图允许节点之间的更多成对交互。多层网络和超图的理论仍处于初级阶段，该项目将利用动力学和光谱特性之间的联系，为它们的分析提供一个新的数学和算法框架。这项数学工作将由图上的共识和意见形成模型来指导我们的问题的形成，并应用于这些模型。此外，我们还将利用图上随机过程的结果来扩展基于图的统计学习，从而开发合并网络和高维样本数据的方法。我们还将在适当的时候使用自然语言处理的工具，将来自在线媒体的文本作为从嵌入中提取的高维向量。所开发的方法将应用于计算社会科学的不同领域。我们将使用动态网络扩展我们在新冠肺炎(脸书数据)下对英国和欧洲人类流动模式的分析，该网络已经揭示了疫情的社会经济后果。此外，我们将通过最近与魏森鲍姆网络社会研究所和柏林自由大学的Barbara Pfetsch教授的合作，深入研究在线舆论的形成，后者研究在线政治团体的两极分化。意识到机器学习的伦理复杂性，我们还旨在通过在透明的贝叶斯框架中评估我们模型的偏差来反思我们应用的伦理。EPSRC研究领域：统计和应用概率、人工智能技术、复杂性科学、ICT中的人类通信、信息系统、自然语言处理主题：数学科学、数字经济、信息和通信技术

The drive towards digitalisation, and the increase in data access and information integration across diverse domains is reinforcing the effects of a networked society. Personal data and actions are now embedded into social networks leading to vast information flows taking place through online social interactions. These new modalities of social links have led to a huge increase in available data of diverse types and scopes, which pose challenges to traditional quantitative methods in the social sciences. Yet they also provide enormous opportunities to gain novel insights through the extraction and analysis of socio-economic data enhanced by social media. To achieve this, we need the development of mathematical and computational techniques based on dynamical and stochastic processes on networks which can capture the key characteristics of social dynamics.This PhD project will investigate new mathematical and algorithmic tools for network analysis that address the challenges posed by big data for a networked society. Methodologically, the project lies at the intersection of network analysis, graph theory, dynamical systems, stochastic processes, and graph-based statistical learning.Networks emerging from the analysis of social data sets have distinct properties that will conform our special focus of research. Specifically, we will focus on methods that can deal with directed weighted graphs; geometrically and geographically constrained networks; and graphs with multi-scale and multi-layer organisation that capture the richness of social interactions and information flows. As a first objective, we will consider the problem of scale selection within methods for multiscale graph partitioning based on random walks (i.e., Markov Stability analysis). This dynamical approach to graph partitioning extends naturally to directed, weighted, multiscale networks, making it highly applicable to social networks. We will then generalise this analysis to multilayer networks, which allow the integration of different kinds of interactions between nodes, and to hypergraphs, which allow for more than pairwise interactions between nodes. The theory of both multilayer networks and hypergraphs is still in its infancy and this project will contribute to a novel mathematical and algorithmic framework for their analysis exploiting the connection between dynamics and spectral properties. This mathematical work will be informed by and applied to models of consensus and opinion formation on graphs guiding the formulation of our problems.Additionally, we will develop methods that merge networks and high-dimensional sample data by extending graph-based statistical learning using results from stochastic processes on graphs. We will also use tools from natural language processing when appropriate to include text from online media as high-dimensional vectors extracted from embeddings. The developed methods will be applied to different domains in the computational social sciences. We will extend our analysis of UK and European human mobility patterns under COVID-19 (Facebook data) using dynamical networks, which has already uncovered insights into the socio-economic consequences of the pandemic. Furthermore, we will pursue an in-depth study of online opinion formation through our recently established collaboration with Prof. Barbara Pfetsch at the Weizenbaum Institute for the Networked Society and the Freie Universitaet in Berlin who studies polarisation of political groups online. Being aware of the ethical complexity of machine learning, we also aim to reflect on the ethics of our applications by evaluating the biases of our models in a transparent Bayesian framework.EPSRC Research areas:Statistics and applied probability,Artificial intelligence technologies, Complexity science, Human communication in ICT, Information systems, Natural language processing Themes:Mathematical sciences,Digital Economy,Information and communication tec