Aug 14, 2021 · In this paper, we devise a novel graph neural network based framework to address this challenging problem, motivated by its great success in ...

This is a PyTorch implementation of the H2MN algorithm, which reasons over a pair of graph-structured objects for graph similarity learning. Specifically, the ...

This paper proposes Hierarchical Hypergraph Matching Networks (H2sup>MN), a novel graph neural network based framework that learns graph representation from ...

MGMN devises a multilevel graph matching network for computing graph similarity, including global-level graphgraph interactions, local-level node-node ...

Nov 16, 2022 · To this end, we propose Hierarchical Hypergraph Matching Networks (H2sup>MN) to calculate the similarities between graph pairs with arbitrary ...

Aug 18, 2021 · We introduce a novel hierarchical hypergraph matching net- work for graph similarity learning, which utilizes hyper- graph to capture the fine- ...

Jul 12, 2022 · H2mn: Graph similarity learning with hierarchical hypergraph matching networks. In SIGKDD, pages 2274–2284, 2021. [Zhu et al., 2020] Yanqiao ...

H2mn: Graph similarity learning with hierarchical hypergraph matching networks ... Learning temporal interaction graph embedding via coupled memory networks.

Hierarchical Graph Pooling with Structure Learning ... H2MN Public. H2MN: Graph Similarity Learning with Hierarchical Hypergraph Matching Networks (KDD-2021).

The proposed Hierarchical Graph Matching Network (HGMN) model consists of a node-graph matching network for effectively learning cross-level interactions ...