Representation Learning on Networks
- William Hamilton
- Rex Ying
- Jure Leskovec
- Rok Sosic
Researchers in network science have traditionally relied on user-defined heuristics to extract features from complex networks (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode network structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. These network representation learning (NRL) approaches remove the need for painstaking feature engineering and have led to state-of-the-art results in network-based tasks, such as node classification, node clustering, and link prediction.
In this tutorial, we will cover key advancements in NRL over the last decade, with an emphasis on fundamental advancements made in the last two years. We will discuss classic matrix factorization-based methods (e.g., Laplacian eigenmaps), random-walk based algorithms (e.g., DeepWalk and node2vec), as well as very recent advancements in graph convolutional networks (GCNs). We will cover methods to embed individual nodes as well as approaches to embed entire (sub)graphs, and in doing so, we will present a unified framework for NRL.