CRII: III: Novel Embedding Algorithms for Attributed Networks
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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.