%0 Generic %1 hamilton2017representation %A Hamilton, William L. %A Ying, Rex %A Leskovec, Jure %D 2017 %K clustering embedding graph structure %T Representation Learning on Graphs: Methods and Applications %U http://arxiv.org/abs/1709.05584 %X Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work.

@misc{hamilton2017representation, abstract = {Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work.}, added-at = {2020-06-19T12:08:36.000+0200}, author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure}, biburl = {https://www.bibsonomy.org/bibtex/2a4b8d0e5abd3c4e320491f9066d64a11/parismic}, description = {1709.05584.pdf}, interhash = {bc3e9a68bb3f8d0dd8b5ecf322bf4aec}, intrahash = {a4b8d0e5abd3c4e320491f9066d64a11}, keywords = {clustering embedding graph structure}, note = {cite arxiv:1709.05584Comment: Published in the IEEE Data Engineering Bulletin, September 2017; version with minor corrections}, timestamp = {2020-06-19T12:08:36.000+0200}, title = {Representation Learning on Graphs: Methods and Applications}, url = {http://arxiv.org/abs/1709.05584}, year = 2017 }