Xingcheng Fu

Orcid: 0000-0002-4643-8126

According to our database1, Xingcheng Fu authored at least 39 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
A comprehensive survey on GNN-based anomaly detection: taxonomy, methods, and the role of large language models.
Int. J. Mach. Learn. Cybern., August, 2025

Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks.
CoRR, July, 2025

DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning.
CoRR, July, 2025

Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning.
CoRR, July, 2025

Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs.
CoRR, May, 2025

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks.
CoRR, May, 2025

Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification.
Proceedings of the ACM on Web Conference 2025, 2025

Graph Size-imbalanced Learning with Energy-guided Structural Smoothing.
Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025

ST-GCond: Self-supervised and Transferable Graph Dataset Condensation.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Prompt-based Unifying Inference Attack on Graph Neural Networks.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Discrete Curvature Graph Information Bottleneck.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
CausalFD: causal invariance-based fraud detection against camouflaged preference.
Int. J. Mach. Learn. Cybern., November, 2024

Dynamic Graph Information Bottleneck.
Proceedings of the ACM on Web Conference 2024, 2024

GC-Bench: An Open and Unified Benchmark for Graph Condensation.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Hyperbolic Geometric Latent Diffusion Model for Graph Generation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

ReGCL: Rethinking Message Passing in Graph Contrastive Learning.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
A Robust and Generalized Framework for Adversarial Graph Embedding.
IEEE Trans. Knowl. Data Eng., November, 2023

Higher-order memory guided temporal random walk for dynamic heterogeneous network embedding.
Pattern Recognit., November, 2023

Heterogeneous graph neural network with semantic-aware differential privacy guarantees.
Knowl. Inf. Syst., October, 2023

Adaptive curvature exploration geometric graph neural network.
Knowl. Inf. Syst., May, 2023

AIC-GNN: Adversarial information completion for graph neural networks.
Inf. Sci., May, 2023

Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification.
Proceedings of the ACM Web Conference 2023, 2023

Unbiased and Efficient Self-Supervised Incremental Contrastive Learning.
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Does Graph Distillation See Like Vision Dataset Counterpart?
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

POINE<sup>2</sup>: Improving Poincaré Embeddings for Hierarchy-Aware Complex Query Reasoning over Knowledge Graphs.
Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Self-Organization Preserved Graph Structure Learning with Principle of Relevant Information.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Curvature Graph Generative Adversarial Networks.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation.
Proceedings of the IEEE International Conference on Data Mining, 2022

Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Graph Structure Learning with Variational Information Bottleneck.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network.
Proceedings of the IEEE International Conference on Data Mining, 2021

2019
A three-phase approach to differentially private crucial patterns mining over data streams.
Comput. Secur., 2019


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