{"list":"other","total":54,"rows":[{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1803.10122","title":"World Models","created":"2018-03-27","cited_by_count":1513},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"2007.02133","title":"Simple and Deep Graph Convolutional Networks","created":"2020-07-04","cited_by_count":860},{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713},{"id":"1910.12892","title":"Hyperbolic Graph Neural Networks","created":"2019-10-28","cited_by_count":244},{"id":"2310.00149","title":"One for All: Towards Training One Graph Model for All Classification Tasks","created":"2023-09-29","cited_by_count":243},{"id":"2310.13023","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","created":"2023-10-19","cited_by_count":226},{"id":"2308.07134","title":"Language is All a Graph Needs","created":"2023-08-14","cited_by_count":220},{"id":"2402.08170","title":"LLaGA: Large Language and Graph Assistant","created":"2024-02-13","cited_by_count":181},{"id":"2411.14499","title":"Understanding World or Predicting Future? A Comprehensive Survey of World Models","created":"2024-11-21","cited_by_count":166},{"id":"2310.01089","title":"GraphText: Graph Reasoning in Text Space","created":"2023-10-02","cited_by_count":111},{"id":"2402.09834","title":"All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining","created":"2024-02-15","cited_by_count":86},{"id":"2411.06070","title":"GFT: Graph Foundation Model with Transferable Tree Vocabulary","created":"2024-11-09","cited_by_count":85},{"id":"2304.11116","title":"Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT","created":"2023-04-10","cited_by_count":80},{"id":"2403.01121","title":"OpenGraph: Towards Open Graph Foundation Models","created":"2024-03-02","cited_by_count":78},{"id":"2402.07197","title":"GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks","created":"2024-02-11","cited_by_count":63},{"id":"2502.05424","title":"SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation","created":"2025-02-08","cited_by_count":62},{"id":"2410.10329","title":"GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs","created":"2024-10-14","cited_by_count":58},{"id":"2408.10700","title":"AnyGraph: Graph Foundation Model in the Wild","created":"2024-08-20","cited_by_count":53},{"id":"2402.10359","title":"Can we Soft Prompt LLMs for Graph Learning Tasks?","created":"2024-02-15","cited_by_count":52},{"id":"2104.02228","title":"Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs","created":"2021-04-06","cited_by_count":50},{"id":"2405.13934","title":"Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models","created":"2024-05-22","cited_by_count":42},{"id":"2502.02017","title":"Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment","created":"2025-02-04","cited_by_count":42},{"id":"2405.20445","title":"Fully-inductive Node Classification on Arbitrary Graphs","created":"2024-05-30","cited_by_count":39},{"id":"2404.11568","title":"On the Scalability of GNNs for Molecular Graphs","created":"2024-04-17","cited_by_count":36},{"id":"2502.03251","title":"RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry","created":"2025-02-05","cited_by_count":35},{"id":"2211.17068","title":"Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces","created":"2022-11-30","cited_by_count":32},{"id":"2412.16441","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","created":"2024-12-21","cited_by_count":32},{"id":"2310.04944","title":"Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data","created":"2023-10-07","cited_by_count":25},{"id":"2208.14073","title":"A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning","created":"2022-08-30","cited_by_count":24},{"id":"2401.01232","title":"Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning","created":"2024-01-02","cited_by_count":23},{"id":"2408.00863","title":"UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation","created":"2024-08-01","cited_by_count":23},{"id":"2405.11801","title":"LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering","created":"2024-05-20","cited_by_count":21},{"id":"2501.15755","title":"GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design","created":"2025-01-27","cited_by_count":21},{"id":"2404.14986","title":"$\\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning","created":"2024-04-23","cited_by_count":20},{"id":"2401.12780","title":"DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing","created":"2024-01-23","cited_by_count":16},{"id":"2410.17941","title":"Spiking Graph Neural Network on Riemannian Manifolds","created":"2024-10-23","cited_by_count":16},{"id":"2305.03555","title":"Contrastive Graph Clustering in Curvature Spaces","created":"2023-05-05","cited_by_count":15},{"id":"2505.12684","title":"Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement","created":"2025-05-19","cited_by_count":14},{"id":"2511.05592","title":"GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning","created":"2025-11-05","cited_by_count":13},{"id":"2412.00315","title":"One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs","created":"2024-11-30","cited_by_count":12},{"id":"2502.13562","title":"Are Large Language Models In-Context Graph Learners?","created":"2025-02-19","cited_by_count":12},{"id":"2510.01510","title":"Flock: A Knowledge Graph Foundation Model via Learning on Random Walks","created":"2025-10-01","cited_by_count":9},{"id":"2510.07755","title":"FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling","created":"2025-10-09","cited_by_count":8},{"id":"2407.03953","title":"Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training","created":"2024-07-04","cited_by_count":7},{"id":"2510.17457","title":"Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models","created":"2025-10-20","cited_by_count":7},{"id":"2512.07857","title":"SA$^{2}$GFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentation","created":"2025-11-26","cited_by_count":5},{"id":"2502.03236","title":"Pioneer: Physics-informed Riemannian Graph ODE for Entropy-increasing Dynamics","created":"2025-02-05","cited_by_count":4},{"id":"2504.09970","title":"ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Space","created":"2025-04-14","cited_by_count":4},{"id":"2507.19526","title":"Quantizing Text-attributed Graphs for Semantic-Structural Integration","created":"2025-07-20","cited_by_count":4},{"id":"2506.14098","title":"Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks","created":"2025-06-17","cited_by_count":2},{"id":"2603.00599","title":"Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger","created":"2026-02-28","cited_by_count":2}],"next_cursor":null}