Area of research
Artificial Intelligence · Statistical and Nonlinear Physics
Research interest
Research focused on Graph and Theoretical computer science, with related work in Autoencoder, Recommender system, Interpretability. Notable publications include 'Federated Social Recommendation with Graph Neural Network', 'Sequential Recommendation via Stochastic Self-Attention', and 'SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization'.
Hierarchical Text Classification Optimization via Structural Entropy and Singular Smoothing
Community Detection in Large-Scale Complex Networks via Structural Entropy Game
RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry
A Scalable Algorithm for Fair Influence Maximization with Unbiased Estimator
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Adaptive and Robust DBSCAN With Multi-Agent Reinforcement Learning
CoSENT: Consistent Sentence Embedding via Similarity Ranking
Triplet-aware graph neural networks for factorized multi-modal knowledge graph entity alignment
RicciNet: Deep Clustering via A Riemannian Generative Model
Scalable Semi-Supervised Clustering via Structural Entropy With Different Constraints
Relational Prompt-Based Pre-Trained Language Models for Social Event Detection
DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism
Reinforced GNNs for Multiple Instance Learning
Augmenting Low-Resource Cross-Lingual Summarization with Progression-Grounded Training and Prompting
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
Federated Social Recommendation with Graph Neural Network
Sequential Recommendation via Stochastic Self-Attention
DAGAD: Data Augmentation for Graph Anomaly Detection
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Deep graph level anomaly detection with contrastive learning
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer
Domain-Invariant Feature Progressive Distillation with Adversarial Adaptive Augmentation for Low-Resource Cross-Domain NER
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network