{"total":18,"rows":[{"id":"2405.09591","title":"A Comprehensive Survey on Data Augmentation","created":"2024-05-15","cited_by_count":40,"area":null},{"id":"2305.13825","title":"Continual Learning on Dynamic Graphs via Parameter Isolation","created":"2023-05-23","cited_by_count":36,"area":null},{"id":"2306.04265","title":"Permutation Equivariant Graph Framelets for Heterophilous Graph Learning","created":"2023-06-07","cited_by_count":19,"area":null},{"id":"2401.09769","title":"A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions","created":"2024-01-18","cited_by_count":13,"area":null},{"id":"2409.00687","title":"When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning","created":"2024-09-01","cited_by_count":12,"area":null},{"id":"2308.09499","title":"Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer","created":"2023-08-18","cited_by_count":10,"area":null},{"id":"2306.03624","title":"On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering","created":"2023-06-06","cited_by_count":9,"area":null},{"id":"2310.15003","title":"Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries","created":"2023-10-23","cited_by_count":6,"area":null},{"id":"2403.17351","title":"Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network","created":"2024-03-26","cited_by_count":3,"area":null},{"id":"2508.06034","title":"Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneity","created":"2025-08-08","cited_by_count":3,"area":null},{"id":"2405.18933","title":"LSPI: Heterogeneous Graph Neural Network Classification Aggregation Algorithm Based on Size Neighbor Path Identification","created":"2024-05-29","cited_by_count":2,"area":null},{"id":"2503.08760","title":"Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes","created":"2025-03-11","cited_by_count":2,"area":null},{"id":"2506.20980","title":"Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs","created":"2025-06-26","cited_by_count":2,"area":null},{"id":"2402.08085","title":"Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2409.06323","title":"LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs","created":"2024-09-10","cited_by_count":0,"area":null},{"id":"2411.12330","title":"Graph as a feature: improving node classification with non-neural graph-aware logistic regression","created":"2024-11-19","cited_by_count":0,"area":null},{"id":"2510.05750","title":"Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2606.05046","title":"Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning","created":"2026-06-03","cited_by_count":0,"area":null}],"next_cursor":null}