EAGER: Asynchronous Event Models for State-Topology Co-Evolution of Temporal Networks

本研究项目的目的是开发用于模拟网络演化和动力学的概率模型和相关的机器学习算法。这项研究为科学家控制网络以达到预期的结果奠定了理论基础并提供了实用工具。尽管这项研究具有广泛的适用性，但研究团队主要考虑了两个应用领域：社交网络和P2P小额信贷。在社交网络方面，该项目通过更好地理解和建模用户行为及其对社会关系和社会群体形成的影响，为互联网行业带来了实用价值。对于P2P小额信贷，该项目有可能更好地吸引非营利性贷款人的参与，从而帮助发展中国家的小企业。此外，这项研究为本科生和研究生教育提供了材料和内容，并帮助学生培养解决现实世界问题所需的跨学科思维方式和工具。这项研究旨在为现代应用中产生的网络化、异步和相互依赖的事件流开发机器学习理论和算法。研究人员特别强调，当底层网络结构发生重大变化时，可以处理时间网络的方法。该提案的一个主要主题是对网络节点动态和网络拓扑动态之间的相互作用进行建模，即网络共同进化。研究人员提出了一种基于多变量点过程的事件数据建模和分析框架。该方法极大地扩展了传统机器学习技术的应用范围。一个例子是回答“谁将做什么，什么时候做”这个问题，这对于网络数据分析中的事件序列建模至关重要，在网络数据分析中，传统的机器学习算法难以应用。

The purpose of this research project is to develop probabilistic models and the related machine learning algorithms for modeling network evolution and dynamics. The research lays theoretic foundations and provides practical tools for scientists to control networks in order to achieve desirable outcomes. Although the research is widely applicable, the research team primarily considers two application areas: social networks and P2P microfinance. In social networks, this project brings practical values to the Internet industry by better understanding and modeling of user behaviors and their impacts on social ties and social group formation. For P2P microfinance, this project has the potential to better engage not-for-profit lenders and thus to help small business in developing countries. Furthermore, the research provides materials and contents for both undergraduate and graduate education and helps students develop interdisciplinary mindsets and tools needed to tackle real-world problems. This proposed research aims to develop machine learning theory and algorithms for networked asynchronous and interdependent event streams arising from modern applications. The researchers especially emphasize methodology that can handle temporal networks when the underlying network structures are undergoing substantial changes. One major theme of the proposal is the modeling of the interplay between network node dynamics and network topology dynamics, or network co-evolution. The researchers propose a novel framework based on multivariate point processes for modeling and analyzing event data. The methods significantly expand the application area of conventional machine learning techniques. One example is to answer the question ``who will do what and when'', which is critical to event sequence modeling in network data analysis where traditional machine learning algorithms are difficult to apply.