{"total":23,"rows":[{"id":"2112.09992","title":"Weisfeiler and Leman go Machine Learning: The Story so far","created":"2021-12-18","cited_by_count":122,"area":null},{"id":"2107.12375","title":"Geometric Deep Learning on Molecular Representations","created":"2021-07-26","cited_by_count":113,"area":null},{"id":"2110.00577","title":"Reconstruction for Powerful Graph Representations","created":"2021-10-01","cited_by_count":65,"area":null},{"id":"2204.07697","title":"Theory of Graph Neural Networks: Representation and Learning","created":"2022-04-16","cited_by_count":61,"area":null},{"id":"2206.11168","title":"Ordered Subgraph Aggregation Networks","created":"2022-06-22","cited_by_count":57,"area":null},{"id":"2106.06707","title":"Graph Neural Networks with Local Graph Parameters","created":"2021-06-12","cited_by_count":52,"area":null},{"id":"2212.08174","title":"Non-IID Transfer Learning on Graphs","created":"2022-12-15","cited_by_count":43,"area":null},{"id":"2203.13913","title":"SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks","created":"2022-03-25","cited_by_count":37,"area":null},{"id":"2301.11039","title":"WL meet VC","created":"2023-01-26","cited_by_count":31,"area":null},{"id":"2306.03698","title":"Fine-grained Expressivity of Graph Neural Networks","created":"2023-06-06","cited_by_count":25,"area":null},{"id":"2211.03232","title":"Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks","created":"2022-11-06","cited_by_count":21,"area":null},{"id":"2107.10234","title":"Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks","created":"2021-07-21","cited_by_count":19,"area":null},{"id":"2301.12906","title":"Curvature Filtrations for Graph Generative Model Evaluation","created":"2023-01-30","cited_by_count":19,"area":null},{"id":"2402.07568","title":"Weisfeiler-Leman at the margin: When more expressivity matters","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2211.16494","title":"On the Ability of Graph Neural Networks to Model Interactions Between Vertices","created":"2022-11-29","cited_by_count":12,"area":null},{"id":"2210.00728","title":"Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples","created":"2022-10-03","cited_by_count":10,"area":null},{"id":"2205.10914","title":"Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited","created":"2022-05-22","cited_by_count":9,"area":null},{"id":"2212.02055","title":"Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes","created":"2022-12-05","cited_by_count":7,"area":null},{"id":"2403.11408","title":"Layer-diverse Negative Sampling for Graph Neural Networks","created":"2024-03-18","cited_by_count":7,"area":null},{"id":"2308.09570","title":"Investigating the Interplay between Features and Structures in Graph Learning","created":"2023-08-18","cited_by_count":5,"area":null},{"id":"2302.14806","title":"Framelet Message Passing","created":"2023-02-28","cited_by_count":3,"area":null},{"id":"2208.13427","title":"The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph Classification","created":"2022-08-29","cited_by_count":2,"area":null},{"id":"2105.03994","title":"Dispatcher: A Message-Passing Approach To Language Modelling","created":"2021-05-09","cited_by_count":0,"area":null}],"next_cursor":null}