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Learning Robust Node Representations via Graph Neural Network and Multilayer Perceptron Classifier
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DOI：10.3390/math14040680.png)
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Abstract
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Node classification is a fundamental task in graph-based learning, with applications in social networks, citation networks, and biological systems. Learning node representations for different graph datasets is necessary to find the correlation between different types of nodes. Graph Neural Networks (GNNs) play a critical role in providing revolutionary solutions for graph data structures. In this paper, we analyze the effect of combined GNN and multilayer perceptron (MLP) architecture to solve the node classification problem for different graph datasets. The feature information and network topology are efficiently captured by the GNN layer, and the MLP helps to make accurate decisions. We have selected popular datasets, namely Amazon-computer, Amazon-photo, Citeseer, Cora, Corafull, PubMed, and Wikics, for evaluating the performance of the proposed approach. In addition, in the GNN part, we have used six models to find the best model fit in the proposed architecture. We have conducted extensive simulations to find the node classification accuracy for the proposed model. The results show the proposed architecture can outperform previous studies in terms of test accuracy. In particular, the GNN algorithms SAGEConv, GENConv, and TAGConv show superior performance across different datasets.
Keywords:
graph neural network
node classification
performance measurement
graph data
multilayer perceptron
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