Minh X. Hoang

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Given a function that classifies a data object as relevant or irrelevant, we consider the task of selecting k objects that best represent all relevant objects in the underlying database. This problem occurs naturally when analysts want to familiarize themselves with the relevant objects in a database using a small set of k exemplars. In this paper, we solve(More)
Global-state networks provide a powerful mechanism to model the increasing heterogeneity in data generated by current systems. Such a network comprises of a series of network snapshots with dynamic local states at nodes, and a global network state indicating the occurrence of an event. Mining discriminative subgraphs from global-state networks allows us to(More)
Predicting the movement of crowds in a city is strategically important for traffic management, risk assessment, and public safety. In this paper, we propose predicting two types of flows of crowds in every region of a city based on big data, including human mobility data, weather conditions, and road network data. To develop a practical solution for(More)
We study the nature of missed collaboration opportunities in evolving collaboration networks. We define a k-way missed collaboration as one in which every (k-1)-subset of the k persons has collaborated but the set of k has not. Representing a collaboration network as a simplicial complex, we model a missed collaboration as a Minimal Non Face (MNF). Focusing(More)
Complex network phenomena -- such as information cascades in online social networks -- are hard to fully observe, model, and forecast. In forecasting, a recent trend has been to forgo the use of parsimonious models in favor of models with increasingly large degrees of freedom that are trained to learn the behavior of a process from historical data.(More)
Team science is a collaborative approach to research, typically with researchers drawn from different disciplines. Team science networks have certain unique characteristics in their conception and intent that set them apart from other commonly studied social and collaboration networks. We study the structural properties, and present metrics for(More)
Predicting the popularity of online content in social networks is important in many applications, ranging from ad campaign design, web content caching and prefetching, to websearch result ranking. Earlier studies target this problem by learning models that either generalize behaviors of the entire network population or capture behaviors of each individual(More)
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