Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks

• URL: http://arxiv.org/abs/2106.13061v1
• Date: Thu, 24 Jun 2021 14:36:50 GMT
• Title: Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
• Abstract summary: We introduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation. The results show that there exists high correlation between some of the structural features. A redundant feature combination with initial node features, which is filtered by graph neural network has improved its classification accuracy in some graph datasets.
• Score: 7.766921168069533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structural features are important features in graph datasets. However, although there are some correlation analysis of features based on covariance, there is no relevant research on exploring structural feature correlation on graphs with graph neural network based models. In this paper, we introduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network. The results show that there exists high correlation between some of the structural features. A redundant feature combination with initial node features, which is filtered by graph neural network has improved its classification accuracy in some graph datasets. We compare the difference between concatenation methods on connecting embeddings between features and show that the simplest is the best. We generalize on the synthetic geometric graphs and certify the results on prediction difficulty between two structural features.

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