4 citations · 7 across the 10 of their papers we have counts for
11 papers
Self-Referential -SAT and the Finite Analogue of Gödel's Incompleteness Theorem
Wen Fang, Xianxian Li, Jun Liu +3
We study self-reference and solution independence in Boolean -SAT as a finite analogue of Gödel incompleteness. Independence is defined through factorial moments of the solution…
Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space
Xingcheng Fu, Shengpeng Wang, Yisen Gao +4
Knowledge Tracing (KT) diagnoses students' concept mastery through continuous learning state monitoring in education.Existing methods primarily focus on studying behavioral sequenc…
Constructing self-referential instances for the clique problem
Jiaqi Li, Shuli Hu, Xianxian Li +1
In this paper, we propose constructing self-referential instances to reveal the inherent algorithmic hardness of the clique problem. First, we prove the existence of a phase transi…
Toward a Unified Geometry Understanding: Riemannian Diffusion Framework for Graph Generation and Prediction
Yisen Gao, Xingcheng Fu, Qingyun Sun +2
Graph diffusion models have made significant progress in learning structured graph data and have demonstrated strong potential for predictive tasks. Existing approaches typically e…
Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
Yudan Song, Yuecen Wei, Yuhang Lu +6
Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improve…
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
Jinyan Wang, Liu Yang, Yuecen Wei +5
Graph Neural Network-based methods face privacy leakage risks due to the introduction of topological structures about the targets, which allows attackers to bypass the target's pri…
Discrete Curvature Graph Information Bottleneck
Xingcheng Fu, Jian Wang, Yisen Gao +4
Graph neural networks(GNNs) have been demonstrated to depend on whether the node effective information is sufficiently passing. Discrete curvature (Ricci curvature) is used to stud…
Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
Xingcheng Fu, Yisen Gao, Beining Yang +4
Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data…
Hyperbolic Geometric Latent Diffusion Model for Graph Generation
Xingcheng Fu, Yisen Gao, Yuecen Wei +4
Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation…
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Yuecen Wei, Haonan Yuan, Xingcheng Fu +4
Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational…
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation
Yuecen Wei, Xingcheng Fu, Qingyun Sun +4
Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspe…