{"total":20,"rows":[{"id":"2402.02216","title":"Position: Graph Foundation Models are Already Here","created":"2024-02-03","cited_by_count":86,"area":null},{"id":"2404.11568","title":"On the Scalability of GNNs for Molecular Graphs","created":"2024-04-17","cited_by_count":36,"area":null},{"id":"2407.07457","title":"GLBench: A Comprehensive Benchmark for Graph with Large Language Models","created":"2024-07-10","cited_by_count":36,"area":null},{"id":"2406.14969","title":"Uni-Mol2: Exploring Molecular Pretraining Model at Scale","created":"2024-06-21","cited_by_count":20,"area":null},{"id":"2402.02054","title":"Towards Neural Scaling Laws on Graphs","created":"2024-02-03","cited_by_count":13,"area":null},{"id":"2407.11624","title":"Rethinking Fair Graph Neural Networks from Re-balancing","created":"2024-07-16","cited_by_count":11,"area":null},{"id":"2505.16379","title":"Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey","created":"2025-05-22","cited_by_count":10,"area":null},{"id":"2409.01081","title":"Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization","created":"2024-09-02","cited_by_count":9,"area":null},{"id":"2410.13761","title":"GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning","created":"2024-10-17","cited_by_count":8,"area":null},{"id":"2502.16284","title":"MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra","created":"2025-02-22","cited_by_count":8,"area":null},{"id":"2505.22109","title":"The quest for the GRAph Level autoEncoder (GRALE)","created":"2025-05-28","cited_by_count":8,"area":null},{"id":"2411.01158","title":"Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction","created":"2024-11-02","cited_by_count":4,"area":null},{"id":"2406.16935","title":"Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex","created":"2024-06-16","cited_by_count":3,"area":null},{"id":"2408.01018","title":"GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs","created":"2024-08-02","cited_by_count":3,"area":null},{"id":"2410.21683","title":"Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer","created":"2024-10-29","cited_by_count":3,"area":null},{"id":"2506.17345","title":"CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning","created":"2025-06-19","cited_by_count":3,"area":null},{"id":"2502.07027","title":"Representational Alignment with Chemical Induced Fit for Molecular Relational Learning","created":"2025-02-07","cited_by_count":2,"area":null},{"id":"2606.08574","title":"OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework","created":"2026-06-07","cited_by_count":2,"area":null},{"id":"2602.11618","title":"How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks?","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2510.21088","title":"M-GLC: Motif-Driven Global-Local Context Graphs for Few-shot Molecular Property Prediction","created":"2025-10-24","cited_by_count":0,"area":null}],"next_cursor":null}