Graphs and hypergraphs are prevalent in many real world applications arising from online social and financial platforms, recommendation systems and knowledge bases. How to represent such graph data to capture their similarities or differences between nodes and graphs? How to integrate graph data with other sources of data in representation learning? How to combine deep learning with symbolic reasoning? How to better design algorithms over graphs?  

I will present a graph neural network framework for addressing these challenges based on the idea of embedding message passing algorithms into function spaces, and learning these algorithms from data. In large scale applications involving molecule design, recommendation system and knowledge reasoning, this graph neural network framework consistently achieves the-state-of-the-art results, in terms of accuracy, model size and scalability. Graph Neural Networks also appear to be a very good tool to advance AI to the next stage, which can combine deep learning with symbolic reasoning.  

参考翻译：

《用于表示学习和符号推理的图神经网络》

图形和超图在许多现实世界的应用中非常流行，这些应用源于在线社交和金融平台、推荐系统和知识库。如何表示这样的图形数据以捕捉节点和图形之间的相似性或差异？在表示学习中如何将图形数据与其他数据源集成？如何将深度学习与符号推理结合起来？如何在图上更好地设计算法？

基于将消息传递算法嵌入到函数空间并从数据中学习这些算法的思想，我将提出一个图神经网络框架来解决这些挑战。在涉及分子设计、推荐系统和知识推理的大规模应用中，该图神经网络框架在精确性、模型规模和可扩展性方面始终达到了最新的水平。图神经网络似乎也是将人工智能推进到下一阶段的一个很好的工具，它可以将深度学习与符号推理结合起来。