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Many real data come in the form of non-grid objects, i.e. graphs, from social networks to molecules. Adaptation of deep learning from grid-alike data (e.g. images) to graphs has recently received unprecedented attention from both machine learning and data mining communities, leading to a new cross-domain field---Deep Graph Learning (DGL). Instead of painstaking feature engineering, DGL aims to learn informative representations of graphs in an end-to-end manner. It has exhibited remarkable success in various tasks, such as node/graph classification, link prediction, etc.

In this tutorial, we aim to provide a comprehensive introduction to deep graph learning. We first introduce the theoretical foundations on deep graph learning with a focus on describing various Graph Neural Network Models (GNNs). We then cover the key achievements of DGL in recent years. Specifically, we discuss the four topics: 1) training …