A histogram-based approach to calculate graph similarity using graph neural networks

Abstract

Deep learning has revolutionized the field of pattern recognition and machine learning by exhibiting exceptional efficiency in recognizing patterns. The success of deep learning can be seen in a wide range of applications including speech recognition, natural language processing, video processing, and image classification. Moreover, it has also been successful in recognizing structural patterns, such as graphs. Graph Neural Networks (GNNs) are models that employ message passing between nodes in a graph to capture its dependencies. These networks memorize a state that approximates graph information with greater depth compared to traditional neural networks. Although training a GNN can be challenging, recent advances in GNN variants, including Graph Convolutional Neural Networks, Gated Graph Neural Networks, and Graph Attention Networks, have shown promising results in solving various problems. In this work, we present a GNN-based approach for computing graph similarity and demonstrate its application to a classification problem. Our proposed method converts the similarity of two graphs into a score, and experiments on state-of-the-art datasets show that the proposed technique is effective and efficient. Results are summarized using a confusion matrix and mean square error metric, demonstrating the accuracy of our proposed technique.