III: Small: Learning Latent Representations of Heterogeneous Information Networks

特征工程是在科学研究和商业应用的所有领域中应用机器学习算法进行知识发现的重要预处理步骤。在这些应用中，获得最能描述所观察到的现象的适当特征至关重要。传统上，研究人员通常根据领域专家的知识和经验手动确定感兴趣的特征，这是昂贵的和劳动密集型的。最近，一个新的研究方向，称为表征学习，已经使用神经网络来自动学习可能用于各种科学研究项目和商业应用的特征。PI计划新的表示学习方法，以捕获丰富的，有意义的和歧视性的特征，在异构信息网络（HIN），已被用来建模异构类型的网络实体及其关系，支持网络数据分析和挖掘。在这个项目中计划的工作包括信息模型设计，可扩展性，样本数据提取，网络多样性和数据异构性问题的学习框架的实施。该研究将被整合到数据挖掘和机器学习的研究生和本科生课程中，使学生能够培养分析和大数据技能。该项目的具体研究目标有三个方面：1）PI旨在利用HIN中的信息来学习网络中由元路径指定的节点和关系的潜在特征的表示。将开发新的技术来解决学习中的可扩展性问题。2)PI旨在解决HIN随时间增长而出现的模型设计和学习问题，例如，引文网络将设计新的神经网络结构和新的样本数据提取方案。3)PI计划在HIN的表征学习中整合内容和网络结构。将设计新的神经网络架构。为了评估研究原型，PI将开发一个由新的神经网络框架组成的测试平台，用于在HIN上进行表示学习。技术和软件将作为研究资源提供给数据挖掘和表示学习社区。

Feature engineering is an important pre-processing step in applying machine learning algorithms for knowledge discovery in all fields of scientific research and business applications. In these applications it is crucial to obtain appropriate features that best describe the observed phenomena. Traditionally, researchers often manually decide features of interest based on the knowledge and experiences of domain experts, which is costly and labor-intensive. Recently, a new line of research, called representation learning, has used neural networks to automatically learn features that may be used in various scientific research projects and business applications. The PI plans new representation learning methods to capture rich, meaningful and discriminative features in heterogeneous information networks (HINs), which have been used to model heterogeneous types of network entities and their relationships in support of network data analysis and mining. The work planned in this project includes information about model design, scalability, sample data extraction, network variety and data heterogeneity issues in the implementation of the learning frameworks. This research will be integrated into graduate and undergraduate courses of data mining and machine learning, enabling students to develop analytics and big data skills.The specific research objectives of this project are three-fold: 1) The PI aims to leverage information in HINs to learn representations of latent features for nodes and relationships specified by meta-paths in the network. Novel techniques will be developed to address the scalability issues in learning. 2) The PI seeks to address model design and learning issues arising in HINs growing with time, e.g., citation networks. New neural network architectures and new sample data extraction schemes will be devised. 3) The PI plans to integrate both content and network structures in representation learning of HINs. New neural network architectures will be devised. To evaluate research prototypes, the PI will develop a testbed consisting of new neural network frameworks for representation learning on HINs. Techniques and software will be made available as research resources to the communities of data mining and representation learning.