Deep Graph Learning
Yu Rong, Tingyang Xu, Junzhou Huang, Wenbing Huang, Hong Cheng, Yao Ma, Yiqi Wang, Tyler Derr, Lingfei Wu, Tengfei Ma
Abstract
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.
Topics & Concepts
Computer scienceFeature engineeringDeep learningGridDomain adaptationGraphArtificial intelligenceFeature learningMachine learningTheoretical computer scienceData scienceMathematicsGeometryClassifier (UML)Advanced Graph Neural NetworksGraph Theory and AlgorithmsRecommender Systems and Techniques