{"total":19,"rows":[{"id":"2302.07090","title":"A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests","created":"2023-02-14","cited_by_count":64,"area":null},{"id":"2401.08514","title":"Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness","created":"2024-01-16","cited_by_count":39,"area":null},{"id":"2302.11556","title":"Equivariant Polynomials for Graph Neural Networks","created":"2023-02-22","cited_by_count":38,"area":null},{"id":"2304.07702","title":"An Empirical Study of Realized GNN Expressiveness","created":"2023-04-16","cited_by_count":33,"area":null},{"id":"2302.05743","title":"Is Distance Matrix Enough for Geometric Deep Learning?","created":"2023-02-11","cited_by_count":22,"area":null},{"id":"2306.03266","title":"Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman","created":"2023-06-05","cited_by_count":13,"area":null},{"id":"2402.07099","title":"Rethinking the Capacity of Graph Neural Networks for Branching Strategy","created":"2024-02-11","cited_by_count":12,"area":null},{"id":"2310.19142","title":"MAG-GNN: Reinforcement Learning Boosted Graph Neural Network","created":"2023-10-29","cited_by_count":11,"area":null},{"id":"2402.06087","title":"Descriptive Kernel Convolution Network with Improved Random Walk Kernel","created":"2024-02-08","cited_by_count":5,"area":null},{"id":"2406.12841","title":"Demystifying Higher-Order Graph Neural Networks","created":"2024-06-18","cited_by_count":4,"area":null},{"id":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2312.08671","title":"Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?","created":"2023-12-14","cited_by_count":2,"area":null},{"id":"2508.18515","title":"Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study","created":"2025-08-25","cited_by_count":2,"area":null},{"id":"2410.03517","title":"Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2402.08085","title":"Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2412.02136","title":"Graph Learning for Planning: The Story Thus Far and Open Challenges","created":"2024-12-03","cited_by_count":0,"area":null},{"id":"2510.13615","title":"Message Passing on the Edge: Towards Scalable and Expressive GNNs","created":"2025-10-15","cited_by_count":0,"area":null},{"id":"2602.01553","title":"Plain Transformers are Surprisingly Powerful Link Predictors","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.01388","title":"Invariant-Stratified Propagation for Expressive Graph Neural Networks","created":"2026-03-02","cited_by_count":0,"area":null}],"next_cursor":null}