Mining Complex Interconnected Data

连接在不同的领域无处不在;从员工通过电子邮件进行通信，到蛋白质和基因相互作用以执行生物功能。该计划旨在通过设计模型和算法来研究相互关联的数据，这些模型和算法在制定和解决现实世界的问题时集成了两个互补的信息源：连接和内容。例如，当调查在线伴游广告中的人口贩运活动时，我们希望考虑从每个广告内容中提取的指标，例如，对未成年受害者的引用;以及不同的广告如何相互连接，例如，使用共享的电话号码，这可能会揭示一个有组织的活动。 为了聚合连接和内容，该提案借鉴了网络科学和机器学习的思想;后者传统上专注于内容（单个数据点的特征），而前者主要研究它们之间的连接。虽然在这两者之间存在多条研究路线，但仍然需要大量的努力来实现我们在现实世界中遇到的复杂数据的表达性，通用性，可扩展性和时间依赖性建模。 更具体地说，该提案追求三个目标，为互联数据建立更丰富的模型：（i）在现实世界的特征丰富的图形中找到特定领域和通用的模式;（ii）重新定义和统一机器学习和网络科学中的经典问题和技术，以进行互联数据分析;（iii）形式化和解决超出现有框架的新任务。我计划通过调查这三个目标在三个应用领域的更广泛影响来实现这三个目标：数据驱动的反人口贩运，计算社会科学和在线学习生态系统。在这三个应用程序中，相互关联的数据分析是理解和解决手头问题的关键。该研究计划提供了一个及时的计划，以推进研究新兴现象所需的理论基础，如在线社会，并通过跨学科的方法解决当今世界的问题，如在线人口贩运，该方法结合了网络科学，机器学习和数据挖掘的最新进展。

Connections are ubiquitous in different domains; from employees communicating by emails, to proteins and genes interacting to carry out a biological function. This program aims to study interconnected data by designing models and algorithms that integrate two complementary sources of information when formulating and solving real-world problems: connection and content. For example, when investigating human trafficking activity in online escort advertisement, we want to consider indicators extracted from the content of each ad, e.g., references to an underage victim; as well as how different ads are connected to each other, e.g., using a shared phone number, which may reveal an organized activity. To aggregate connection and content, this proposal draws on ideas from network science and machine learning; where the latter domain has been traditionally focused on the content (features of individual datapoints), and the former area primarily studies the connections between them. While there exist multiple lines of research in between, substantial effort is still needed to enable expressive, general, scalable, and time-dependent modelling of the complex data we encounter in real-world settings. In more concrete terms, this proposal pursues three objectives towards richer models for interconnected data: (i) finding domain-specific and universal patterns in real-world feature-rich graphs; (ii) redefining and unifying classical problems and techniques in machine learning and network science for interconnected data analysis, and (iii) formalizing and addressing novel tasks that are beyond the existing frameworks. I plan to navigate these three objectives by investigating their broader impacts within three application domains: data-driven counter human trafficking, computational social science, and online learning ecosystems. In these three applications, interconnected data analysis holds the key to understanding and solving the problems at hand. This research program provides a timely plan to advance the theoretical foundations needed for the study of emerging phenomena, such as online societies, and addressing today's world's problems, such as online human trafficking, through an interdisciplinary approach that unifies the recent progress in network science, machine learning and data mining.