Representation Learning with Relational Data

图是一种普遍存在的数据结构，广泛应用于计算机科学及相关领域。社会网络、分子图结构、生物蛋白质-蛋白质网络、推荐系统以及所有这些领域都可以很容易地建模为图，这些图捕捉单个实体(即节点)之间的关系(即边)。然而，图不仅仅用作结构化的知识库：许多机器学习应用程序寻求使用图结构的数据作为输入来进行预测或发现新的模式。例如，人们可能希望对蛋白质在生物相互作用图中的角色进行分类，向社交网络中的用户推荐新朋友，或者预测其结构可以用图表示的现有药物分子的新治疗应用。 利用图进行机器学习的中心问题是找到一种方法，将有关图结构的信息编码到机器学习模型中。例如，在社交网络中的链接预测的情况下，人们可能想要编码节点之间的成对属性，例如关系强度或共同朋友的数量。或者在对蛋白质在生物相互作用网络中的作用进行分类的情况下，人们可能想要包括关于蛋白质的局部图形邻域的结构的信息。 在我的研究计划中，我将探索一种新的、但正在快速发展的图机器学习方法：基于图表示学习(GRL)的方法。这些方法背后的关键思想是将节点或整个(子)图作为点嵌入到学习的低维向量空间中，并使用神经网络来推理这些学习的向量空间中的关系交互。传统方法会在应用标准机器学习算法之前提取图统计数据作为预处理步骤，而GRL方法直接使用端到端方式的图结构数据进行学习。虽然这些方法仍处于初级阶段，但在许多图形分析任务中显示出了相当大的前景。 由于图结构数据的无处不在，GRL具有广泛的潜在应用，从化学合成到社会网络分析。在之前的研究中，我开发了GRL方法来预测药物与疾病的相互作用，在网络论坛中对复杂的社交互动进行建模，并为Pinterest Inc.的生产规模推荐系统提供支持。在未来几年，我将继续追求这些核心应用主题，特别是与计算社会科学和计算生物学相关的应用。许多生物学和社会科学领域的科学家现在拥有海量的结构化数据，但缺乏有效理解和使用这些数据的计算工具。我的研究的一个关键重点将是开发基于GRL的模型，该模型可以帮助这些领域专家利用这些数据进行大规模的知识发现。

Graphs are a ubiquitous data structure employed extensively within computer science and related fields. Social networks, molecular graph structures, biological protein-protein networks, recommender systemsall of these domains and many more can be readily modeled as graphs, which capture relations (i.e., edges) between individual entities (i.e., nodes). However, graphs are not only useful as structured knowledge repositories: many machine learning applications seek to make predictions or discover new patterns using graph-structured data as input. For example, one might wish to classify the role of a protein in a biological interaction graph, recommend new friends to a user in a social network, or predict new therapeutic applications of existing drug molecules whose structure can be represented as a graph. The central problem in machine learning with graphs is finding a way to encode information about graph-structure into a machine learning model. For example, in the case of link prediction in a social network, one might want to encode pairwise properties between nodes, such as relationship strength or the number of common friends. Or in the case of classifying a protein's role in a biological interaction network, one might want to include information about the structure of the protein's local graph neighborhood. In my research program, I will explore a nascent, but quickly developing class of approaches to machine learning with graphs: approaches based on graph representation learning (GRL). The key idea behind these approaches is to embed nodes, or entire (sub)graphs, as points in a learned low-dimensional vector space and to use neural networks to reason about relational interactions in these learned vector spaces. Whereas traditional approaches would extract graph statistics as a pre-processing step before applying standard machine learning algorithms, GRL approaches directly learn using graph-structured data in an end-to-end fashion. While still in their nascency, these methods have shown considerable promise across numerous graph analysis tasks. Due to the ubiquity of graph-structured data, GRL has wide range of potential applications, ranging from chemical synthesis to social network analysis. In previous research, I have developed GRL methods to predict drug-disease interactions, to model complex social interactions in web forums, and to power a production-scale recommender system at Pinterest Inc. Over the coming years, I will continue to pursue these core application themes, especially applications related to computational social science and computational biology. Many domain scientists in biology and the social sciences now possess massive troves of structured data but lack the computational tools to effectively understand and use it. A key focus of my research will be developing GRL-based models that can aid such domain experts in leveraging this data for large-scale knowledge discovery.