# An efficient scheme based on graph centrality to select nodes for training for effective learning

arXiv: https://arxiv.org/abs/2104.14123 · HTML: https://mlsanity.com/paper/2104.14123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning](https://mlsanity.com/paper/2007.16002/) (2020)
- [Identifying a set of influential spreaders in complex networks](https://mlsanity.com/paper/1602.00070/) (2016)
- [Diffusion-based Deep Active Learning](https://mlsanity.com/paper/2003.10339/) (2020)
- [Confidence-based Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1901.08255/) (2019)

## Abstract (arXiv)

The process of selecting points for training a machine learning model is often a challenging task. Many times, we will have a lot of data, but for training, we require the labels and labeling is often costly. So we need to select the points for training in an efficient manner so that the model trained on the points selected will be better than the ones trained on any other training set. We propose a novel method to select the nodes in graph datasets using the concept of graph centrality. Two methods are proposed - one using a smart selection strategy, where the model is required to be trained only once and another using active learning method. We have tested this idea on three popular graph datasets - Cora, Citeseer and Pubmed- and the results are found to be encouraging.