An end-to-end heterogeneous network for graph similarity learning
2022
At present, the similarity learning methods used in many deep models can only capture the local context relations among samples in small cliques (e.g. pairs and triplets), so it is difficult to construct and leverage their latent global correlations to further boost the learning of discriminant features. To this end, we propose a novel end-to-end
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