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arXiv · 1810.10866

Convolutional Set Matching for Graph Similarity

Yunsheng Bai·Hao Ding·Yizhou Sun·Wei Wang
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Abstract

We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs. As the core operation of graph similarity search, pairwise graph similarity computation is a challenging problem due to the NP-hard nature of computing many graph distance/similarity metrics. We demonstrate our model using the Graph Edit Distance (GED) as the example metric. Experiments on three real graph datasets demonstrate that our model achieves the state-of-the-art performance on graph similarity search.

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Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang. 2018-10-23. Convolutional Set Matching for Graph Similarity. https://arxiv.org/abs/1810.10866

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