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.

归因网络在各种现实世界中无处不在，例如社交媒体，学术网络，医疗保健系统和企业系统。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，研究人员彼此合作，并通过其独特的研究兴趣或个人资料与众不同；在社交网络中，用户与他人进行互动和通信，并发布一些个性化内容。作为分析网络的有效计算工具，网络嵌入是一种学习网络中每个节点的低维表示的技术。这样的表示在支持各种网络分析应用程序（包括社区检测，链接预测和网络可视化）中起着至关重要的作用。尽管大多数专注于简单网络嵌入的研究，但该项目的目的是通过解决由大规模且复杂的属性网络数据带来的挑战来开发归因网络的新型嵌入算法。该项目的结果将是一类新的理论和实际网络嵌入方法，以分析大型和复杂的网络数据。开发的算法将灵活地适应社会计算，健康信息学和企业系统中的各种工业应用。该项目还将开发一个新的课程，结合了拟议的研究。此外，该项目将使PI继续为本科生，女性和代表性不足的学生提供研究机会的持续努力。该项目的目的是开发高效有效的网络嵌入算法，以处理包含复杂网络交互的大型网络。从开放网络信息系统中进行的数据，本研究将从两个角度（即可扩展网络嵌入和利用网络交互）解决属性网络分析的问题。具体而言，该项目旨在通过两个主要的研究目标来实现目标：（1）通过从异质信息网络和多视图学习观点开发两个配方，以及它们相应的快速优化算法，从而在大规模归因网络上执行有效嵌入； （2）通过利用社会理论，例如社会地位分析和社会身份理论来改变现有的网络嵌入算法。项目网站（http://faculty.cs.tamu.edu/xiahu/projects-crii.html）可访问更多信息和结果，包括出版物，软件，数据集和课程材料。

项目成果

Exploring Expert Cognition for Attributed Network Embedding

• DOI: 10.1145/3159652.3159655
• 发表时间: 2018-01-01
• 期刊: WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING
• 作者: Huang, Xiao;Song, Qingquan;Hu, Xia
• 通讯作者: Hu, Xia