专题：主题演讲

All real-world data has structures that are best described as graphs. If there is one data structure for deep learning algorithms, graph would be the foremost candidate. The graph structure can be either explicit, such in social networks, knowledge graphs, and protein-interaction networks, etc., or latent and implicit, as in the case of languages and images. Leveraging and discovering graph structures have many immediate applications and also serves as a fertile ground for tnew generation of algorithms.

This talk begins with a general survey of deep graph learning, and then we will discuss a few new research work at AWS Shanghai AI Lab in this direction. We will introduce DGL, an open-source platform designed to accelerate research in this new emerging field, with its philosophy to support graph as the core abstraction and take care to maintain both forward (i.e. supporting new research ideas) and backward (i.e. integration with existing components) compatibility. DGL enables arbitrary message handling and mutation operators, flexible propagation rules, and is framework agnostic so as to leverage high-performance tensor, autograd operations, and other feature extraction modules already available in existing frameworks. DGL carefully handles the sparse and irregular graph structure, deals with graphs big and small which may change dynamically, fuses operations, and performs auto-batching, all to take advantages of modern hardware. DGL has been tested on a variety of models, including but not limited to the popular Graph Neural Networks (GNN) and its variants, with promising speed, memory footprint and scalability. 

We are actively applying DGL to a number of domains, working with customers both internal and external to AWS, as well as pushing research of deep graph of its own right. Since its launch during NeuIPS'18, DGL has evolved rapidly. The latest release supports heterograph and knowledge graph, a significant step towards real-world applications.

现实世界的数据并不是孤立存在的。因此，描述数据最好的数据结构就是图。换句话说，如果深度学习作为一个算法，那么图是其支撑的最好的数据结构。图的结构可以是显式的，比如社交网络、知识图、药、蛋白质、关系数据库，等等。图也可以是隐式隐蔽在语言和图像背后。利用好、发掘出图结构既有丰富的现实场景，也为下一代机器学习算法提供了重要的基础。