{"total":41,"rows":[{"id":"2108.06040","title":"Knowledge Graph Reasoning with Relational Digraph","created":"2021-08-13","cited_by_count":92,"area":null},{"id":"2201.08236","title":"Temporal Knowledge Graph Completion: A Survey","created":"2022-01-16","cited_by_count":61,"area":null},{"id":"2102.10062","title":"A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective","created":"2021-02-19","cited_by_count":38,"area":null},{"id":"2209.07084","title":"Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling","created":"2022-09-15","cited_by_count":18,"area":null},{"id":"2112.01064","title":"AutoGEL: An Automated Graph Neural Network with Explicit Link Information","created":"2021-12-02","cited_by_count":17,"area":null},{"id":"2402.17238","title":"Does Negative Sampling Matter? A Review with Insights into its Theory and Applications","created":"2024-02-27","cited_by_count":15,"area":null},{"id":"2104.10796","title":"Efficient Non-Sampling Knowledge Graph Embedding","created":"2021-04-21","cited_by_count":14,"area":null},{"id":"2202.13785","title":"CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion","created":"2022-02-25","cited_by_count":13,"area":null},{"id":"2205.00904","title":"Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion","created":"2022-05-02","cited_by_count":13,"area":null},{"id":"2102.07186","title":"Relation-aware Graph Attention Model With Adaptive Self-adversarial Training","created":"2021-02-14","cited_by_count":12,"area":null},{"id":"2104.01488","title":"Medical Entity Disambiguation Using Graph Neural Networks","created":"2021-04-03","cited_by_count":10,"area":null},{"id":"2402.19195","title":"Negative Sampling in Knowledge Graph Representation Learning: A Review","created":"2024-02-29","cited_by_count":9,"area":null},{"id":"2304.11618","title":"Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding","created":"2023-04-23","cited_by_count":7,"area":null},{"id":"2310.09781","title":"Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding","created":"2023-10-15","cited_by_count":7,"area":null},{"id":"2111.11026","title":"Learning Explicit User Interest Boundary for Recommendation","created":"2021-11-22","cited_by_count":6,"area":null},{"id":"2212.13847","title":"Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples","created":"2022-12-28","cited_by_count":6,"area":null},{"id":"2202.09606","title":"MixKG: Mixing for harder negative samples in knowledge graph","created":"2022-02-19","cited_by_count":5,"area":null},{"id":"2507.20498","title":"Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning","created":"2025-07-28","cited_by_count":5,"area":null},{"id":"2205.07427","title":"Exploring the Learning Difficulty of Data Theory and Measure","created":"2022-05-16","cited_by_count":4,"area":null},{"id":"2210.06242","title":"Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph Embedding","created":"2022-10-12","cited_by_count":4,"area":null},{"id":"2303.00286","title":"Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction","created":"2023-03-01","cited_by_count":3,"area":null},{"id":"2305.10563","title":"HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding","created":"2023-05-17","cited_by_count":3,"area":null},{"id":"2112.10372","title":"A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods","created":"2021-12-20","cited_by_count":2,"area":null},{"id":"2203.04703","title":"LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings","created":"2022-03-09","cited_by_count":2,"area":null},{"id":"2306.07512","title":"Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning","created":"2023-06-13","cited_by_count":2,"area":null},{"id":"2204.06520","title":"Bayesian Negative Sampling for Recommendation","created":"2022-04-02","cited_by_count":1,"area":null},{"id":"2210.15495","title":"Leveraging Wikidata's edit history in knowledge graph refinement tasks","created":"2022-10-27","cited_by_count":1,"area":null},{"id":"2309.13227","title":"Importance of negative sampling in weak label learning","created":"2023-09-23","cited_by_count":1,"area":null},{"id":"2403.03993","title":"Personalized Negative Reservoir for Incremental Learning in Recommender Systems","created":"2024-03-06","cited_by_count":1,"area":null},{"id":"2407.17070","title":"Curriculum Negative Mining For Temporal Networks","created":"2024-07-24","cited_by_count":1,"area":null},{"id":"2501.15393","title":"Diffusion-based Hierarchical Negative Sampling for Multimodal Knowledge Graph Completion","created":"2025-01-26","cited_by_count":1,"area":null},{"id":"2504.03327","title":"Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction","created":"2025-04-04","cited_by_count":1,"area":null},{"id":"2506.05626","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","created":"2025-06-05","cited_by_count":1,"area":null},{"id":"2508.05587","title":"Enhancing PyKEEN with Multiple Negative Sampling Solutions for Knowledge Graph Embedding Models","created":"2025-08-07","cited_by_count":1,"area":null},{"id":"2408.00326","title":"Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity","created":"2024-08-01","cited_by_count":0,"area":null},{"id":"2410.04488","title":"A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion","created":"2024-10-06","cited_by_count":0,"area":null},{"id":"2410.07592","title":"Diversified and Adaptive Negative Sampling on Knowledge Graphs","created":"2024-10-10","cited_by_count":0,"area":null},{"id":"2411.14858","title":"Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models","created":"2024-11-22","cited_by_count":0,"area":null},{"id":"2605.27023","title":"Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2608.23849","title":"FlowNeg: GFlowNet-Guided Diverse Hard Negative Sampling for Knowledge Graph Embedding","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2608.30652","title":"PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN","created":"2026-08-31","cited_by_count":0,"area":null}],"next_cursor":null}