Automated Machine Learning on Graphs: A Survey
arXiv:2103.00742 · doi:10.24963/ijcai.2021/637
Abstract
Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in-depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.
IJCAI 2021 Survey Track. Change bib style for better retrieval and add paper collection URL
References in corpus (18)
- Practical Bayesian Optimization of Machine Learning Algorithms
- Neural Architecture Search with Reinforcement Learning
- Fast Graph Representation Learning with PyTorch Geometric
- The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
- Predicting multicellular function through multi-layer tissue networks
- NAS-Bench-101: Towards Reproducible Neural Architecture Search
- Automated Machine Learning: State-of-The-Art and Open Challenges
- Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
- Learning Disentangled Representations for Recommendation
- DIG: A Turnkey Library for Diving into Graph Deep Learning Research
- Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
- Graph Structure of Neural Networks
- Learned Low Precision Graph Neural Networks
- Evolutionary Architecture Search for Graph Neural Networks
- A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks
- Simplifying Architecture Search for Graph Neural Network
- CogDL: A Comprehensive Library for Graph Deep Learning
- JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms