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

属性网络在各种现实世界系统中无处不在，例如社交媒体，学术网络，医疗保健系统和企业系统。属性网络不同于传统网络，传统网络中只表示节点和链接，因为这些网络中的节点也与丰富的属性集相关联。例如，在学术网络中，研究人员相互合作，并通过其独特的研究兴趣或个人资料与其他人区分开来;在社交网络中，用户与其他人互动和交流，并发布一些个性化的内容。网络嵌入作为分析网络的一种有效的计算工具，是一种学习网络中每个节点的低维表示的技术。这种表示在支持各种网络分析应用中起着至关重要的作用，包括社区检测，链接预测和网络可视化。虽然大多数现有的研究集中在简单的网络嵌入，本项目的目的是通过解决大规模和复杂的属性网络数据带来的挑战，开发新的嵌入算法的属性网络。该项目的成果将是一种新的理论和实用的网络嵌入方法，用于分析大型复杂的网络数据。所开发的算法将是灵活的，以适应促进社会计算，健康信息学和企业系统中的各种工业应用。该项目还将制定一个新的课程，纳入拟议的研究。此外，本项目将使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。本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归因网络分析的问题，即，可扩展的网络嵌入和利用网络交互。具体而言，该项目旨在通过两个主要研究目标来实现这一目标：（1）通过从异构信息网络和多视图学习角度开发两种公式，以及相应的快速优化算法，在大规模属性网络上执行高效嵌入;以及（2）利用社会理论，例如，社会地位分析和社会认同理论。项目网站（http：//faculty.cs.tamu.edu/xiahu/projects-crii.html）提供了进一步的信息和成果，包括出版物、软件、数据集和课程材料。

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.