Shypd.aiThe largest open index of AI toolsSimGNN is an open-source PyTorch implementation for fast graph similarity computation using neural networks. It provides an efficient method for comparing graph structures, offering a significant time reduction over traditional approaches.
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SimGNN is a PyTorch implementation of a novel neural network approach designed for fast graph similarity computation, as detailed in the WSDM 2019 paper. It addresses the computational burden of traditional methods like Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) while maintaining high performance. The tool employs a learnable embedding function to map graphs into embedding vectors, providing a global summary. A key feature is its attention mechanism, which emphasizes important nodes for specific similarity metrics. Additionally, SimGNN includes a pairwise node comparison method to supplement graph-level embeddings with fine-grained node-level information. This approach leads to better generalization on unseen graphs and offers quadratic time complexity in the worst case. Experimental results demonstrate its effectiveness and efficiency, achieving smaller error rates and significant time reductions compared to existing baselines.
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