Graph Neural Networks for the Global Economy with Microsoft DeepGraph

Abstract

Graph Neural Networks (GNNs) are AI models that learn embeddings for the nodes in a graph and use the embeddings to perform prediction tasks. In this talk, we present how we developed GNNs for the LinkedIn economic graph. LinkedIn economic graph is a digital representation of the global economy with 1B nodes and 200B edges, consisting of social graphs about members' connections, activity graphs between members and other economic entities, and knowledge graphs about members', companies', job postings' attributes. By applying GNN to this graph, we can utilize the full potential of the economic graph in many search and recommendation products across LinkedIn. The biggest challenge was to scale up GNNs to a massive scale of billion nodes and edges. To address this challenge, we developed Microsoft DeepGraph, an open source library for large scale GNN development. DeepGraph allows for training GNNs on large graphs by serving the graph in a distributed fashion with graph engine servers. In this talk, we will highlight the strengths of DeepGraph, such as support for both PyTorch and TensorFlow, and integration with Azure ML and Azure Kubernetes Service. We will share lessons and findings from developing GNNs for various applications around the LinkedIn economic graph. We will explain how we combine graphs such as social graph, activity graph, knowledge graphs into one gigantic heterogeneous graph, and what algorithms we employed for this heterogenous graph. We will present a few case studies, such as how we identify job postings with vague titles and replace them with more specific titles using GNNs.