In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings.

In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings without the ...

The model, Graph-Sim, achieves the state-of-the-art performance on four real-world graph datasets under six out of eight settings, compared to existing ...

This is the repo for Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching (AAAI 2020), and Convolutional Set ...

In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings without the ...

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A method for graph similarity learning is devised in this study, namely, Contrastive Graph Similarity Network (CGSim).

Gu, Y. Sun, and W. Wang. Learning-based efficient graph similarity computation via multi-scale convolutional set matching. In AAAI, 2020.

Learning-based efficient graph similarity computation via multi-scale convolutional set matching. Y Bai, H Ding, K Gu, Y Sun, W Wang.

We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs.