CAREER: Human-Centric Big Network Embedding

职业：以人为本的大网络嵌入

• 批准号: 1750074
• 负责人: Xia Hu
• 金额: $ 50万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750074&HistoricalAwards=false
• 关键词: CAREER; Human; Centric; Big; Network

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.

网络嵌入是为了学习低维表示，以促进网络分析应用程序，包括节点分类和网络可视化。该项目是为了研究一个新的方向，以探讨人类知识如何增强网络嵌入以及人类如何更好地理解结果。人类知识代表在这种情况下可以与所学的嵌入相关的任何上下文信息或先验知识。这项多学科研究的成功结果将导致在使领域专家以人类知识的交互和轻松分析大型网络数据方面的进步，从而积极影响各种信息系统的整体价值。综合数据科学教育计划是培训具有重要但不可用的数据分析技术的学生，以吸引代表性不足的团体成员从事工程学的职业，并保留这些群体的成员。该项目的研究目标是开发一个以人为中心的框架来建模和将人类知识纳入网络嵌入，应对大型网络带来的数据挑战，并启用网络嵌入结果的解释和相互作用。该项目开发了一系列与数据驱动方法不同的网络嵌入模型和算法，以分析来自各个方面的网络数据。研究了多视图学习和深层结构化框架，以将节点，边缘和社区级别的三种类型的人类知识整合到统一的框架中。鉴于现实世界网络可以包含异质，大规模和动态的人类知识的事实，因此开发了相应的解决方案来解决这些问题。为了促进人类对研究结果的理解，该项目开发了全球和地方解释算法，以解释网络嵌入和交互式学习算法以整合用户反馈。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。