Mining Complex Interconnected Data

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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.