{"total":30,"rows":[{"id":"2307.10485","title":"FinGPT: Democratizing Internet-scale Data for Financial Large Language Models","created":"2023-07-19","cited_by_count":67,"area":null},{"id":"2402.15183","title":"GraphEdit: Large Language Models for Graph Structure Learning","created":"2024-02-23","cited_by_count":33,"area":null},{"id":"2310.02156","title":"Probabilistically Rewired Message-Passing Neural Networks","created":"2023-10-03","cited_by_count":26,"area":null},{"id":"2312.16560","title":"Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching","created":"2023-12-27","cited_by_count":24,"area":null},{"id":"2406.11945","title":"GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models","created":"2024-06-17","cited_by_count":17,"area":null},{"id":"2405.17311","title":"Probabilistic Graph Rewiring via Virtual Nodes","created":"2024-05-27","cited_by_count":15,"area":null},{"id":"2308.05309","title":"Homophily-enhanced Structure Learning for Graph Clustering","created":"2023-08-10","cited_by_count":9,"area":null},{"id":"2506.11844","title":"TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks","created":"2025-06-13","cited_by_count":5,"area":null},{"id":"2409.05755","title":"Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning","created":"2024-09-09","cited_by_count":4,"area":null},{"id":"2502.12618","title":"Uncertainty-Aware Graph Structure Learning","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2406.01066","title":"Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2410.12096","title":"Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning","created":"2024-10-15","cited_by_count":3,"area":null},{"id":"2411.14711","title":"Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods","created":"2024-11-22","cited_by_count":3,"area":null},{"id":"2412.15735","title":"Prompt-based Unifying Inference Attack on Graph Neural Networks","created":"2024-12-20","cited_by_count":3,"area":null},{"id":"2506.01728","title":"Principled Data Augmentation for Learning to Solve Quadratic Programming Problems","created":"2025-06-02","cited_by_count":2,"area":null},{"id":"2406.08897","title":"Motif-driven Subgraph Structure Learning for Graph Classification","created":"2024-06-13","cited_by_count":1,"area":null},{"id":"2501.05015","title":"On Measuring Unnoticeability of Graph Adversarial Attacks: Observations, New Measure, and Applications","created":"2025-01-09","cited_by_count":1,"area":null},{"id":"2510.22289","title":"Does Homophily Help in Robust Test-time Node Classification?","created":"2025-10-25","cited_by_count":1,"area":null},{"id":"2602.05667","title":"Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2602.19330","title":"CTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis","created":"2026-02-22","cited_by_count":1,"area":null},{"id":"2501.00773","title":"OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks","created":"2025-01-01","cited_by_count":0,"area":null},{"id":"2503.12157","title":"Weighted Graph Structure Learning with Attention Denoising for Node Classification","created":"2025-03-15","cited_by_count":0,"area":null},{"id":"2505.12411","title":"It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph","created":"2025-05-18","cited_by_count":0,"area":null},{"id":"2505.23875","title":"A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants","created":"2025-05-29","cited_by_count":0,"area":null},{"id":"2604.20736","title":"F\\textsuperscript{2}LP-AP: Fast \\& Flexible Label Propagation with Adaptive Propagation Kernel","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.06814","title":"Model-to-Data Distillation for Graph Neural Networks","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.07577","title":"Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.16809","title":"Informative Graph Structure Learning","created":"2026-05-16","cited_by_count":0,"area":null},{"id":"2606.03068","title":"Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2608.11431","title":"AutoGrable: What Is a Good Graph for a Table?","created":"2026-08-11","cited_by_count":0,"area":null}],"next_cursor":null}