{"total":17,"rows":[{"id":"2302.02941","title":"On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology","created":"2023-02-06","cited_by_count":166,"area":null},{"id":"2301.09505","title":"Rethinking the Expressive Power of GNNs via Graph Biconnectivity","created":"2023-01-23","cited_by_count":119,"area":null},{"id":"2301.12906","title":"Curvature Filtrations for Graph Generative Model Evaluation","created":"2023-01-30","cited_by_count":19,"area":null},{"id":"2406.04336","title":"On the Expressive Power of Spectral Invariant Graph Neural Networks","created":"2024-06-06","cited_by_count":15,"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":"2406.05815","title":"What Can We Learn from State Space Models for Machine Learning on Graphs?","created":"2024-06-09","cited_by_count":13,"area":null},{"id":"2406.05482","title":"Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks","created":"2024-06-08","cited_by_count":6,"area":null},{"id":"2404.13604","title":"CKGConv: General Graph Convolution with Continuous Kernels","created":"2024-04-21","cited_by_count":5,"area":null},{"id":"2504.20430","title":"Learning Laplacian Positional Encodings for Heterophilous Graphs","created":"2025-04-29","cited_by_count":4,"area":null},{"id":"2506.05530","title":"Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2410.16699","title":"Graph Transformers Dream of Electric Flow","created":"2024-10-22","cited_by_count":2,"area":null},{"id":"2509.22259","title":"Rotary Position Encodings for Graphs","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2502.18350","title":"Graph Inference with Effective Resistance Queries","created":"2025-02-25","cited_by_count":1,"area":null},{"id":"2402.06908","title":"Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions","created":"2024-02-10","cited_by_count":0,"area":null},{"id":"2406.07574","title":"Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2606.13671","title":"Understanding Truncated Positional Encodings for Graph Neural Networks","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2608.04460","title":"CLEARMIND: Closed-Form Laplacian Embeddings of Arakelov-Green Resistance for Morphological Intrinsic Neuronal Distances","created":"2026-08-05","cited_by_count":0,"area":null}],"next_cursor":null}