{"total":75,"rows":[{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"area":null},{"id":"2204.08570","title":"A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability","created":"2022-04-18","cited_by_count":118,"area":null},{"id":"2212.05767","title":"A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal","created":"2022-12-12","cited_by_count":106,"area":null},{"id":"2310.11829","title":"Graph Foundation Models: Concepts, Opportunities and Challenges","created":"2023-10-18","cited_by_count":103,"area":null},{"id":"2209.14107","title":"Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure","created":"2022-09-28","cited_by_count":83,"area":null},{"id":"2205.10053","title":"What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders","created":"2022-05-20","cited_by_count":82,"area":null},{"id":"2306.16424","title":"Realistic Synthetic Financial Transactions for Anti-Money Laundering Models","created":"2023-06-22","cited_by_count":50,"area":null},{"id":"2203.00949","title":"GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation","created":"2022-03-02","cited_by_count":46,"area":null},{"id":"2401.11720","title":"Graph Condensation: A Survey","created":"2024-01-22","cited_by_count":23,"area":null},{"id":"2408.08685","title":"Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?","created":"2024-08-16","cited_by_count":23,"area":null},{"id":"2308.16375","title":"A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications","created":"2023-08-31","cited_by_count":22,"area":null},{"id":"2310.02152","title":"Graph Neural Network-based EEG Classification: A Survey","created":"2023-10-03","cited_by_count":21,"area":null},{"id":"2308.14522","title":"Graph Meets LLMs: Towards Large Graph Models","created":"2023-08-28","cited_by_count":20,"area":null},{"id":"2202.10793","title":"PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs","created":"2022-02-22","cited_by_count":18,"area":null},{"id":"2301.12230","title":"Continual Graph Learning: A Survey","created":"2023-01-28","cited_by_count":18,"area":null},{"id":"2202.03195","title":"More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks","created":"2022-02-07","cited_by_count":15,"area":null},{"id":"2304.11534","title":"Graph Neural Networks for Text Classification: A Survey","created":"2023-04-23","cited_by_count":15,"area":null},{"id":"2304.08928","title":"ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees","created":"2023-04-18","cited_by_count":12,"area":null},{"id":"2402.06330","title":"Continual Learning on Graphs: A Survey","created":"2024-02-09","cited_by_count":12,"area":null},{"id":"2406.03569","title":"GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications","created":"2024-06-05","cited_by_count":10,"area":null},{"id":"2401.11074","title":"On The Temporal Domain of Differential Equation Inspired Graph Neural Networks","created":"2024-01-20","cited_by_count":9,"area":null},{"id":"2503.21422","title":"From Deep Learning to LLMs: A survey of AI in Quantitative Investment","created":"2025-03-27","cited_by_count":9,"area":null},{"id":"2407.03953","title":"Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training","created":"2024-07-04","cited_by_count":7,"area":null},{"id":"2501.00762","title":"Residual connections provably mitigate oversmoothing in graph neural networks","created":"2025-01-01","cited_by_count":7,"area":null},{"id":"2305.15622","title":"GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint","created":"2023-05-25","cited_by_count":6,"area":null},{"id":"2306.00012","title":"Graph Neural Network for spatiotemporal data: methods and applications","created":"2023-05-30","cited_by_count":6,"area":null},{"id":"2402.00299","title":"Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction","created":"2024-02-01","cited_by_count":5,"area":null},{"id":"2305.16364","title":"E2EAI: End-to-End Deep Learning Framework for Active Investing","created":"2023-05-25","cited_by_count":4,"area":null},{"id":"2311.05764","title":"Generative Explanations for Graph Neural Network: Methods and Evaluations","created":"2023-11-09","cited_by_count":4,"area":null},{"id":"2402.08593","title":"Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2410.20679","title":"MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU","created":"2024-09-25","cited_by_count":4,"area":null},{"id":"2208.06322","title":"EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model","created":"2022-08-12","cited_by_count":3,"area":null},{"id":"2308.04947","title":"Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey","created":"2023-08-09","cited_by_count":3,"area":null},{"id":"2409.09858","title":"A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View","created":"2024-09-15","cited_by_count":3,"area":null},{"id":"2410.00660","title":"Stabilizing the Kumaraswamy Distribution","created":"2024-10-01","cited_by_count":3,"area":null},{"id":"2410.07222","title":"Computing Systemic Risk Measures with Graph Neural Networks","created":"2024-09-30","cited_by_count":3,"area":null},{"id":"2508.04894","title":"Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)","created":"2025-08-06","cited_by_count":3,"area":null},{"id":"2602.20418","title":"CITED: A Decision Boundary-Aware Signature for GNNs Towards Model Extraction Defense","created":"2026-02-23","cited_by_count":3,"area":null},{"id":"2607.26699","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","created":"2026-07-29","cited_by_count":3,"area":null},{"id":"2210.12462","title":"Factor Investing with a Deep Multi-Factor Model","created":"2022-10-22","cited_by_count":2,"area":null},{"id":"2302.13915","title":"TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter","created":"2023-02-27","cited_by_count":2,"area":null},{"id":"2304.13010","title":"Unstructured and structured data: Can we have the best of both worlds with large language models?","created":"2023-04-25","cited_by_count":2,"area":null},{"id":"2407.12112","title":"A Benchmark for Fairness-Aware Graph Learning","created":"2024-07-16","cited_by_count":2,"area":null},{"id":"2410.04039","title":"BlockScan: Detecting Anomalies in Blockchain Transactions","created":"2024-10-05","cited_by_count":2,"area":null},{"id":"2410.09295","title":"Natural Language Counterfactual Explanations for Graphs Using Large Language Models","created":"2024-10-11","cited_by_count":2,"area":null},{"id":"2502.18334","title":"Structural Alignment Improves Graph Test-Time Adaptation","created":"2025-02-25","cited_by_count":2,"area":null},{"id":"2504.18600","title":"QuantBench: Benchmarking AI Methods for Quantitative Investment","created":"2025-04-24","cited_by_count":2,"area":null},{"id":"2202.00408","title":"Dimensionality Reduction Meets Message Passing for Graph Node Embeddings","created":"2022-02-01","cited_by_count":1,"area":null},{"id":"2307.08433","title":"From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs","created":"2023-07-17","cited_by_count":1,"area":null},{"id":"2308.02914","title":"Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy","created":"2023-08-05","cited_by_count":1,"area":null},{"id":"2403.00030","title":"GraphPub: Generation of Differential Privacy Graph with High Availability","created":"2024-02-28","cited_by_count":1,"area":null},{"id":"2502.08353","title":"Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy","created":"2025-02-12","cited_by_count":1,"area":null},{"id":"2506.03087","title":"Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models","created":"2025-06-03","cited_by_count":1,"area":null},{"id":"2512.22772","title":"GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks","created":"2025-12-28","cited_by_count":1,"area":null},{"id":"2601.14633","title":"Relational Graph Modeling for Credit Default Prediction: Heterogeneous GNNs and Hybrid Ensemble Learning","created":"2026-01-21","cited_by_count":1,"area":null},{"id":"2211.05581","title":"Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling","created":"2022-10-26","cited_by_count":0,"area":null},{"id":"2408.02899","title":"SETN: Stock Embedding Enhanced with Textual and Network Information","created":"2024-08-06","cited_by_count":0,"area":null},{"id":"2410.19978","title":"Global Graph Counterfactual Explanation: A Subgraph Mapping Approach","created":"2024-10-25","cited_by_count":0,"area":null},{"id":"2412.05735","title":"REGE: A Method for Incorporating Uncertainty in Graph Embeddings","created":"2024-12-07","cited_by_count":0,"area":null},{"id":"2412.17961","title":"Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study","created":"2024-12-23","cited_by_count":0,"area":null},{"id":"2506.00191","title":"Heterogeneous Graph Backdoor Attack","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2506.07288","title":"EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network","created":"2025-06-08","cited_by_count":0,"area":null},{"id":"2506.08347","title":"Differentially Private Relational Learning with Entity-level Privacy Guarantees","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2508.10785","title":"Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2509.06600","title":"PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2512.17936","title":"Risk-Aware Financial Forecasting Enhanced by Machine Learning and Intuitionistic Fuzzy Multi-Criteria Decision-Making","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2601.15474","title":"BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2603.09564","title":"a-TMFG: Scalable Triangulated Maximally Filtered Graphs via Approximate Nearest Neighbors","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2603.10559","title":"A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2604.10212","title":"Relational Probing: LM-to-Graph Adaptation for Financial Prediction","created":"2026-04-11","cited_by_count":0,"area":null},{"id":"2607.12067","title":"Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.22287","title":"Efficient Recommendations via Graph Coarsening and Label Propagation","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2608.17715","title":"Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models","created":"2026-08-18","cited_by_count":0,"area":null},{"id":"2608.25365","title":"PaSta: Noisy Node Classification with Partial Label Learning","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.33314","title":"ZeroGAR: Benchmarking the Adversarial Robustness of Zero-Shot Graph Models","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}