BIGDATA: F: Collaborative Research: Taming Big Networks via Embedding

在互联网时代，信息实体和客体相互联系，从而形成巨大的信息网络。最近，网络嵌入方法被证明对网络上的许多数据挖掘和机器学习问题有很大的好处。网络嵌入方法创建的低维特征表示保持了数据点在原始空间中的结构。尽管研究取得了长足的进展，但我们仍然缺乏强大的网络嵌入技术来有效地处理海量、异质、复杂和动态的网络。PI旨在开发用于分析海量网络的新一代网络嵌入方法。该研究项目具有显著改变图挖掘和网络分析的潜力。投资促进机构还计划开发将信息网络分析和机器学习相结合的开放课程材料和开放源码软件工具。该项目由四个协同研究推力组成。首先，它开发了基于模型的网络嵌入，以利用网络的一阶和二阶邻近性。其次，设计了一系列既能利用链接信息又能利用边信息的感应网络嵌入方法。第三，针对网络的局部性、非线性等复杂结构，提出了基于局部聚类和深度学习的网络嵌入方法。第四，针对不同的网络嵌入方式开发了在线和随机优化算法，以应对现代海量网络的快速增长和演化。该项目中开发的新方法具有更快的优化收敛速度、更低的计算复杂性和统计学习保证。目标应用包括但不限于社会/信息网络分析中的语义搜索和信息检索、书目数据库中的专家查找以及推荐系统。

In the Internet Age, information entities and objects are interconnected, thereby forming gigantic information networks. Recently, network embedding methods, that create low-dimensional feature representations that preserve the structure of data points in their original space, have been shown to be greatly beneficial for many data mining and machine learning problems over networks. Despite significant research progress, we are still lacking powerful network embedding techniques with theoretical guarantees to effectively deal with massive, heterogeneous, complex and dynamic networks. The PIs aim to develop a new generation of network embedding methods for analyzing massive networks. The research project has the potential to significantly transform graph mining and network analysis. The PIs also plan to develop open course materials and open source software tools that integrate information network analysis and machine learning. This project consists of four synergistic research thrusts. First, it develops model-based network embedding to leverage the first-order and second-order proximity of networks. Second, it devises a family of inductive network embedding methods that are able to leverage both linkage information and side information. Third, it develops both local clustering and deep learning based network embedding methods to attack the complex structure of networks such as locality and non-linearity. Fourth, it develops online and stochastic optimization algorithms for different network embedding methods to tackle the fast growth and evolution of modern massive networks. The new methods developed in this project enjoy faster rates of convergence in optimization, lower computational complexities, and statistical learning guarantees. The targeted applications include but are not limited to semantic search and information retrieval in social/information network analysis, expert finding in bibliographical database, and recommendation systems.