Data Efficient Learning on Graphs

Chuxu Zhang, Jundong Li, Meng Jiang

Abstract: Prevailing methods of graph representation learning (GRL) usually rely on learning from "big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with "small'' labeled data as data annotation and labeling is always a time and resource consuming task. The fact overshadows GRL's capability and applicability for many real situations. Therefore, data efficient learning on graphs has become essential for many real-world applications and there have been many studies working on this topic in recent years. In this tutorial, we will systematically review recent studies of data efficient learning on graphs, in particular a series of methods and applications of graph few-shot learning and graph self-supervised learning. At first, we will introduce the overview of graph representation learning methods, conventional few-shot learning, and self-supervised learning techniques. Then, we will present the work of data efficient learning on graphs in terms of three major graph mining tasks at different granularity levels: node-level learning tasks, graph-level learning tasks, and edge-level learning tasks. In the end, we will conclude the tutorial and raise open problems and pressing issues in future research. The authors of this tutorial are active and productive researchers in this research area.