CAREER: Human-Centric Big Network Embedding

网络嵌入就是学习低维表示，以方便网络分析应用，包括节点分类和网络可视化。这个项目旨在探索一个新的方向，探索人类知识如何增强网络嵌入，以及结果如何能够更好地被人类理解。人类知识表示可以与在该上下文中学习的嵌入相关的任何上下文信息或先验知识。这一多学科研究的成功成果将使领域专家能够利用人类知识交互、轻松地分析大网络数据，从而对各种信息系统的整体价值产生积极影响。综合数据科学教育项目旨在用关键但高度缺乏的数据分析技术培训学生，吸引代表不足的群体的成员投身工程行业，并留住这些群体的成员。该项目的研究目标是开发一个以人为中心的框架，用于在网络嵌入中建模和纳入人类知识，应对大型网络带来的数据挑战，以及使网络嵌入结果能够解释和交互。该项目开发了一系列不同于数据驱动方法的网络嵌入模型和算法，从多个方面分析网络数据。研究了多视角学习和深层结构框架，将节点、边缘和社区三种类型的人类知识集成到一个统一的框架中。鉴于现实世界网络可能包含异质、大规模和动态的人类知识，针对这些问题，提出了相应的解决方案。为了促进人们对研究结果的理解，该项目开发了全球和本地解释算法来解释网络嵌入和互动学习算法，以整合用户反馈。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.