Prescriptive Analytics over Graphs, Streams, and Sequences

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Advances in machine learning (ML) have fueled many successful applications of predictive analytics and recommender systems. Propelling these successes to the next level calls for prescriptive analytics, with 2 key functionalities: i) query over predictions made by models and ii) recommend intervention actions, which if taken, may lead to desired outcomes as predicted by the models. A major long-term vision of this program is to develop a framework, theory, models, and algorithms to realize these functionalities, instantiated on 3 key applications: A) viral marketing (VM); B) misinformation containment; and C) intervention recommendations over medical trajectory databases. The applications have been chosen carefully so that they are related but diverse, they feature heterogeneous data such as graphs, streams, and sequences, and will serve to illustrate the generality of the models and techniques that we will develop. Given snapshots of an ongoing VM campaign, a marketer would want to know if the outcome is likely to meet the sales target. Further, if the product encounters new competition from rival companies, she might want to identify complementary products, which when bundled with that product could help boost the revenue. Finding which posts currently propagating in a social network (e.g., Twitter) are likely to become very similar to, say the propagation traces of "#Pizzagate", is a prediction query. Such posts are good candidates for fake news. If further analysis reveals them as fake, we can take intervention actions to contain them. Consider a medical trajectory database (DB), where a trajectory is a sequence of timestamped observations/measurements on patients. The task, of finding which patients in the DB are most likely to need Oxygen treatment within 6 months of their admission, is strongly related to a top-k query over predictions. An expert would like to find interventions that can minimize this likelihood. In this program, we aim to develop a generic framework for querying over predictions from models and for recommending interventions. Both are novel directions of inquiry, not addressed before, and are certain to break new ground in data science and decision making. Here are some possible example instantiations of our techniques. For A), unlike existing works, we will capture complex interactions between competing and complementary items, using a novel utility-driven model. For B), we will develop ML models of fake content, fact check claims by querying knowledge graphs, and counter misinformation via interventions and mitigation campaigns. For C), we will develop predictive models for medical trajectories and develop strategies for recommending interventions, by combining hypothetical reasoning with predictive analytics. The applications will impact different aspects of society: marketing, fighting fake news, and interventions over medical trajectories. Our techniques will put science front and center in the applications.