CRII: III: Novel Embedding Algorithms for Large-Scale and Complex Attributed Networks

CRII：III：大规模和复杂属性网络的新颖嵌入算法

• 批准号: 1657196
• 负责人: Xia Hu
• 金额: $ 17.5万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657196&HistoricalAwards=false
• 关键词: CRII; III; Novel; Embedding; Algorithms

Attributed networks are ubiquitous in a variety of real-world systems such as social media, academic networks, health care systems and enterprise systems. Attributed networks differ from traditional networks where only nodes and links are represented, as the nodes in these networks are also associated with a rich set of attributes. For example, in academic networks, researchers collaborate with each other and are distinct from others by their unique research interests or profiles; in social networks, users interact and communicate with others and also post some personalized contents. As an effective computational tool in analyzing networks, network embedding is a technique for learning a low-dimensional representation for each node in the network. Such a representation plays an essential role in supporting a variety of network analysis applications including community detection, link prediction and network visualization. While most existing studies focused on simple network embedding, the aim of this project is to develop novel embedding algorithms for attributed networks by tackling challenges brought by large-scale and complex attributed network data. The results of this project will be a new class of theoretical as well as practical network embedding methods to analyze large and complex network data. The developed algorithms will be flexible to be adapted for facilitating various industrial applications in Social Computing, Health Informatics and Enterprise Systems. This project will also develop a new curriculum that incorporates the proposed research. In addition, this project will allow the PI to continue the ongoing efforts of providing research opportunities to undergraduate students, female and underrepresented students.The goal of this project is to develop efficient and effective network embedding algorithms to deal with large-scale attributed networks that contain complex network interactions. Given data from open networked information systems, this research will address the problem of attributed network analytics from two perspectives, i.e., scalable network embedding and leveraging network interactions. Specifically, this project aims to achieve the goal through two primary research objectives: (1) performing efficient embedding on large-scale attributed networks by developing two formulations from heterogeneous information networks and multi-view learning perspectives, as well as their corresponding fast optimization algorithms; and (2) transforming existing network embedding algorithms by leveraging social theories, e.g., social status analysis and social identity theory. The project web site (http://faculty.cs.tamu.edu/xiahu/projects-crii.html) provides access to further information and results, including publications, software, datasets and curriculum materials.

属性网络在社会媒体、学术网络、医疗保健系统和企业系统等各种现实世界系统中无处不在。属性网络与仅表示节点和链接的传统网络不同，因为这些网络中的节点也与一组丰富的属性相关联。例如，在学术网络中，研究人员相互合作，并因其独特的研究兴趣或概况而与其他人区别开来；在社交网络中，用户与他人进行互动和交流，也发布一些个性化的内容。网络嵌入是一种学习网络中每个节点的低维表示的技术，是分析网络的一种有效的计算工具。这种表示在支持社区检测、链路预测和网络可视化等各种网络分析应用中起着至关重要的作用。现有的研究大多集中在简单的网络嵌入上，而本项目的目标是通过应对大规模、复杂的属性网络数据带来的挑战，开发新的属性网络嵌入算法。该项目的成果将为分析大型复杂网络数据提供一种新的理论和实用的网络嵌入方法。开发的算法将是灵活的，以适应促进各种工业应用在社会计算，健康信息和企业系统。该项目还将开发一个包含拟议研究的新课程。此外，该项目将使PI能够继续努力，为本科生、女性和代表性不足的学生提供研究机会。该项目的目标是开发高效和有效的网络嵌入算法，以处理包含复杂网络交互的大规模属性网络。给定来自开放网络信息系统的数据，本研究将从两个角度解决属性网络分析的问题，即可扩展的网络嵌入和利用网络交互。具体而言，本项目旨在通过两个主要研究目标来实现这一目标：(1)通过开发异构信息网络和多视图学习视角的两种公式及其相应的快速优化算法，在大规模属性网络上实现高效嵌入；(2)利用社会理论，如社会地位分析和社会认同理论，改造现有的网络嵌入算法。项目网站（http://faculty.cs.tamu.edu/xiahu/projects-crii.html）提供更多信息和结果，包括出版物、软件、数据集和课程材料。