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Isomorphism Testing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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To test the power of graph representation learning methods based on Isomorphism Testing
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Covered Forest: Fine-grained generalization analysis of graph neural networks
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
PlanE: Representation Learning over Planar Graphs
A Practical, Progressively-Expressive GNN
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race
Local Graph Embeddings Based on Neighbors Degree Frequency of Nodes
Weisfeiler-Lehman meets Gromov-Wasserstein
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
On Graph Neural Networks versus Graph-Augmented MLPs
Can Graph Neural Networks Count Substructures?
On the equivalence between graph isomorphism testing and function approximation with GNNs
Dimension Reduction via Colour Refinement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.