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Isomorphism Testing

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To test the power of graph representation learning methods based on Isomorphism Testing

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ariesoeteman/hegnn

공식 코드

Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization

https://github.com/benfinkelshtein/CoveredForests

공식 코드

Covered Forest: Fine-grained generalization analysis of graph neural networks

https://github.com/frareba/gradualweisfeilerleman

공식 코드

Gradual Weisfeiler-Leman: Slow and Steady Wins the Race

https://github.com/leichen2018/GNN-Substructure-Counting

공식 코드

Can Graph Neural Networks Count Substructures?

https://github.com/leichen2018/GNN_vs_GAMLP

공식 코드

On Graph Neural Networks versus Graph-Augmented MLPs

https://github.com/leichen2018/Ring-GNN

공식 코드

On the equivalence between graph isomorphism testing and function approximation with GNNs

https://github.com/lingxiaoshawn/kcsetgnn

공식 코드

A Practical, Progressively-Expressive GNN

https://github.com/nslab-cuk/unified-graph-transformer

공식 코드

Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity

https://github.com/tpfi22/spectral-and-combinatorial

공식 코드

Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings

https://github.com/zzysonny/plane

공식 코드

PlanE: Representation Learning over Planar Graphs

https://github.com/nslab-cuk/community-aware-graph-transformer

Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity

https://github.com/nslab-cuk/literalkg

Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.