Graph classification is an important task widely applied to biochemistry, social networks, and other fields. Since it is a data-dependent problem, insufficient training data will deteriorate the performance of graph classification models. To address this issue, various data augmentation methods have been proposed. However, most existing methods tend to destroy the topological features, leading to a negative impact on information propagation and semantics. While the null model generates new data with the same topological features as the original graph and helps capture the latent information based on topology. Hence, combining the null model with data augmentation for graph classification may be useful in helping models learn graph representations. This paper introduces a novel null model-based augmentation technique for graph classification. Specifically, four standard and four approximate null model-based augmentation methods are developed to verify the effectiveness of the technique. Experimental results on benchmark datasets demonstrate significant performance improvements with the proposed technique. Depending on the design mechanisms of the null models, standard augmentation methods outperform the approximate ones. These findings emphasize the critical role of non-trivial features in enhancing augmentation models for different network structures, providing a new perspective on data augmentation for studying graph classification methods. 

Null Model-Based Data Augmentation for Graph Classification

LoyalDE: Improving the performance of Graph Neural Networks with loyal node discovery and emphasis

Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizability of downstream tasks. However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes. In this paper, we study the problem of graph data augmentation for Graph Convolutional Network (GCN) in the context of improving the node embeddings for semi-supervised node classification. Specifically, we conduct cosine similarity based cross operation on the original features to create new graph features, including new node attributes and new graph topologies, and we combine them as new pairwise inputs for specific GCNs. Then, we propose an attentional integrating model to weighted sum the hidden node embeddings encoded by these GCNs into the final node embeddings. We also conduct a disparity constraint on these hidden node embeddings when training to ensure that non-redundant information is captured from different features. Experimental results on five real-world datasets show that our method improves the classification accuracy with a clear margin (+2.5% - +84.2%) than the original GCN model.

Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification

Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizability of downstream tasks. However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes. In this paper, we study the problem of graph data augmentation for Graph Convolutional Network (GCN) in the context of improving the node embeddings for semi-supervised node classification. Specifically, we conduct cosine similarity based cross operation on the original features to create new graph features, including new node attributes and new graph topologies, and we combine them as new pairwise inputs for specific GCNs. Then, we propose an attentional integrating model to weighted sum the hidden node embeddings encoded by these GCNs into the final node embeddings. We also conduct a disparity constraint on these hidden node embeddings when training to ensure that non-redundant information is captured from different features. Experimental results on five real-world datasets show that our method improves the classification accuracy with a clear margin (+2.5%–+84.2%) than the original GCN model.

Data Augmentation for Graph Convolutional Network on Semi-supervised Classification.

Today, there are two major understandings for graph convolutional networks, ie, in the spectral and spatial domain But both lack transparency In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings Inspired by it, we propose a new graph learning paradigm -- Monte Carlo Graph Learning (MCGL) The core idea of MCGL contains: (1) Data augmentation: propagate the labels of the training set through the graph structure and expand the training set; (2) Model training: use the expanded training set to train traditional classifiers We use synthetic datasets to compare the strengths of MCGL and graph convolutional operation on clean graphs In addition, we show that MCGL's tolerance to graph structure noise is weaker than GCN on noisy graphs (four real-world datasets) Moreover, inspired by MCGL, we re-analyze the reasons why the performance of GCN becomes worse when deepened too much: rather than the mainstream view of over-smoothing, we argue that the main reason is the graph structure noise, and experimentally verify our view The code is available at this https URL

Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning.

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Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning.