Abstract: 

A graph is a data structure composed of nodes interconnected by edges. Many real world data can be represented by graphs, in application domains as diverse as social networks to biochemistry. Graph Neural Networks (GNN) are a relatively new type of Deep Learning architecture that have evolved to work effectively with these data structures. Traditional network architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks are designed around the idea of leveraging spatial and temporal locality respectively, and are thus optimized for use in 2-d and 3-d images and sequential data such as text, audio, and time series, which exhibit these properties. GNNs, on the other hand, are designed to work with typical characteristics of graph structure, such as their complex topology and indeterminate size. GNNs are flexible enough to solve different classes of graph tasks, i.e. node level tasks such as node classification, edge level tasks such as link prediction and recommendation, and graph or subgraph level tasks such as finding graph isomorphism, etc. GNNs thus provide an efficient and scalable way to do deep learning against graph structured data and solve novel problems. In this tutorial, we will introduce GNN concepts and popular GNN architectures such as Graph Convolution Network (GCN), GraphSAGE, and Graph Attention Network (GAT), and describe how they can be used to solve different types of graph tasks. We will demonstrate examples of different types of GNN using Pytorch and Pytorch Geometric. Pytorch is a popular library for deep learning in Python, and Pytorch Geometric is a library for doing deep learning specifically on irregular data structures such as graphs. Attendees are expected to come away from the talk with an understanding of GNN features and tools for implementing them. To derive the most value from the talk, attendees should be familiar with Python and Pytorch programming.

Bio: 

Sujit Pal builds intelligent systems around research content that help researchers and medical professionals achieve better outcomes. His areas of interest are Information Retrieval, Natural Language Processing and Machine Learning (including Deep Learning). As an individual contributor in the Elsevier Labs team, he works with diverse product teams to help them solve tough problems in these areas, as well as build proofs of concept at the cutting edge of applied research.