Convolutional Neural Networks for Fast Approximation of Graph Edit Distance
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Graph Edit Distance (GED) computation is a core operation of many widely-used graph applications, such as graph classification, graph matching, and graph similarity search. However, computing the exact GED between two graphs is NP-complete. Most current approximate algorithms are based on solving a combinatorial optimization problem, which involves complicated design and high time complexity. In this paper, we propose a novel end-to-end neural network based approach to GED approximation, aiming to alleviate the computational burden while preserving good performance. The proposed approach, named GSimCNN, turns GED computation into a learning problem. Each graph is considered as a set of nodes, represented by learnable embedding vectors. The GED computation is then considered as a two-set matching problem, where a higher matching score leads to a lower GED. A Convolutional Neural Network (CNN) based approach is proposed to tackle the set matching problem. We test our algorithm on three real graph datasets, and our model achieves significant performance enhancement against state-of-the-art approximate GED computation algorithms.
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