CAREER: Human-Centric Big Network Embedding Grant uri icon

abstract

  • Network embedding is to learn a low-dimensional representation to facilitate network analytics applications including node classification and network visualization. This project is to investigate a novel direction to explore how human knowledge could enhance network embedding and how the results could be better understood by human beings. Human knowledge represents any context information or prior knowledge that could be correlated to the learned embedding in this context. The successful outcome of this multidisciplinary research will lead to advances in enabling domain experts to interactively and easily analyze big network data with human knowledge, and thus positively impacting the overall value of various information systems. The integrated data science education program is to train students with crucial but highly unavailable data analytics technologies, to attract members of underrepresented groups to careers in engineering, and to retain members of those groups.The research goal of this project is to develop a human-centric framework for modeling and incorporating human knowledge in network embedding, tackling data challenges brought by big networks, as well as enabling interpretation and interaction of network embedding results. This project develops a series of network embedding models and algorithms, different from data-driven approaches, to analyze network data from various aspects. Multi-view learning and deep structured frameworks are investigated to integrate three types of human knowledge from the node-, edge- and community-level into a unified framework. Given the fact that real-world networks could contain heterogeneous, large-scale and dynamic human knowledge, corresponding solutions are developed to handle the problems. To facilitate human understanding of the research results, this project develops global and local interpretation algorithms to explain network embedding and interactive learning algorithms to integrate user feedback.This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.

date/time interval

  • 2018 - 2023