ABSTRACT Title of dissertation: AUTHORITY FLOW-BASED RANKING IN HETEROGENEOUS NETWORKS: PREDICTION, PERSONALIZATION, AND LEARNING TO RANK

Many real-world datasets, including biological networks, the Web, and social media, can be effectively modeled as networks or graphs, in which nodes represent entities of interest and links mimic the interactions or relationships among them. Such networks often contain multiple entity or relationship types, which are referred to as heterogeneous networks. Networks also evolve due to the existence of tempo-ral features that characterize the entities or to the temporal relationships among them. Finding important/authoritative entities in real-world networks is a long-standing and well-defined challenge. In this dissertation, I focus on two variants of the problem. The first is the prediction of the ranking of scientific publications in a future state of a citation network. I introduce a new measure labeled the fu-ture PageRank score. I develop FutureRank, a prediction algorithm for predicting the future PageRank scores from the historical network structure, and evaluate the FutureRank algorithm on multiple bibliographic dataset. Next, I focus on personalized ranking in social media. I extend a social media dataset to include relationships (edge types) between authors, blog posts, categorie