Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

We study the robustness to symmetric label noise of GNNs training procedures.
By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task.
Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.
Large datasets are beneficial to modern machine learning models, especially neural networks.
Many studies have shown that the accuracy of machine learning models grows log-linear to the amount of training data BID9 .
Currently, complex machine learning models can only achieve superhuman classification results when trained with a very large dataset.
However, large datasets are usually expensive to collect and create exact label.
One solution to create large datasets is crowdsourcing, but this approach introduces a higher level of labeling error into the datasets as well as requires a lot of human resources BID1 .
As a consequence, neural networks are prone to very high generalization error under noisy label data.
Figure 1 demonstrate the accuracy results of a graph neural network trained on MUTAG dataset.
Training accuracies tend to remain high while testing accuracies degrades as more label noise is added to the training data.
Figure 1: GIN model trained with increasing symmetric label noise.
The generalization gap increases as more noise is introduced to the training labels.Graph neural network (GNN) is a new class of neural networks which learn from graphstructured data.
Typically, GNNs classify graph vertices or the whole graph itself.
Given the input as the graph structure and data (e.g. feature vectors) on each vertex, GNNs training aim to learn a predictive model for classification.
This new class of neural networks enables end-toend learning from a wider range of data format.
In order to build large scale GNNs, it requires large and clean datasets.
Since graph data is arguably harder to label than image data both at vertex-level or graph-level, graph neural networks should have a mechanism to adapt to training label error or noise.In this paper, we take the noise-correction approach to train a graph neural network with noisy labels.
We study two state-of-the-art graph neural network models: Graph Isomorphism Network BID7 and GraphSAGE BID2 .
Both of these models are trained under symmetric artificial label noise and tested on uncorrupted testing data.
We then apply label noise estimation and loss correction techniques BID5 BID9 to propose our denoising graph neural network model (D-GNN).
In this paper, we have introduced the use of loss correction for Graph Neural Networks to deal with symmetric graph label noise.
We experimented on two different practical noise estimatation methods and compare them to the case when we know the exact noise matrix.
Our empirical results show some improvement on noise tolerant when the correction matrix C is correctly estimated.
In practice, we can consider C as a hyperparameter and tune it following some clean validation data.
We apply loss correction to graph neural networks to train a more robust to noise model.