# JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms

arXiv: https://arxiv.org/abs/2101.06427 · HTML: https://mlsanity.com/paper/2101.06427/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 16 Jan 2021.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [A Survey on Network Embedding](https://mlsanity.com/paper/1711.08752/) (2017)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [HARP: Hierarchical Representation Learning for Networks](https://mlsanity.com/paper/1706.07845/) (2017)
- [NetLSD: Hearing the Shape of a Graph](https://mlsanity.com/paper/1805.10712/) (2018)
- [Graph Coarsening with Preserved Spectral Properties](https://mlsanity.com/paper/1802.04447/) (2018)
- [BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning](https://mlsanity.com/paper/1710.03439/) (2017)
- [Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning](https://mlsanity.com/paper/1804.07824/) (2018)
- [A Survey and Taxonomy of Graph Sampling](https://mlsanity.com/paper/1308.5865/) (2013)
- [Using Triangles to Improve Community Detection in Directed Networks](https://mlsanity.com/paper/1404.5874/) (2014)

## Built on by (most cited first, 4 of 4)

- [Automated Machine Learning on Graphs: A Survey](https://mlsanity.com/paper/2103.00742/) (2021)
- [Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions](https://mlsanity.com/paper/2201.01288/) (2022)
- [MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning](https://mlsanity.com/paper/2206.09280/) (2022)
- [GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection](https://mlsanity.com/paper/2404.01578/) (2024)

## Abstract (arXiv)

Network embedding (NE) can generate succinct node representations for massive-scale networks and enable direct applications of common machine learning methods to the network structure. Various NE algorithms have been proposed and used in a number of applications, such as node classification and link prediction. NE algorithms typically contain hyperparameters that are key to performance, but the hyperparameter tuning process can be time consuming. It is desirable to have the hyperparameters tuned within a specified length of time. Although AutoML methods have been applied to the hyperparameter tuning of NE algorithms, the problem of how to tune hyperparameters in a given period of time is not studied for NE algorithms before. In this paper, we propose JITuNE, a just-in-time hyperparameter tuning framework for NE algorithms. Our JITuNE framework enables the time-constrained hyperparameter tuning for NE algorithms by employing the tuning over hierarchical network synopses and transferring the knowledge obtained on synopses to the whole network. The hierarchical generation of synopsis and a time-constrained tuning method enable the constraining of overall tuning time. Extensive experiments demonstrate that JITuNE can significantly improve performances of NE algorithms, outperforming state-of-the-art methods within the same number of algorithm runs.