Area of research
Artificial Intelligence · Statistical and Nonlinear Physics
Research interest
Research interests include Advanced Graph Neural Networks, Complex Network Analysis Techniques, Topic Modeling, and Data Management and Algorithms.
Graph Neural Networks for Graphs With Heterophily: A Survey
Watermarking techniques for large language models: a survey
A survey of multilingual large language models
A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models
Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning
Large Language Model Simulator for Cold-Start Recommendation
Topological Data Analysis in Graph Neural Networks: Surveys and Perspectives
Graph Foundation Models: Concepts, Opportunities and Challenges
Variational Graph Generator for Multiview Graph Clustering
A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models
Mixture of experts (MoE): A big data perspective
Disentangling Inter- and Intra-Cascades Dynamics for Information Diffusion Prediction
A survey on machine unlearning: Techniques and new emerged privacy risks
Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey
LLGformer: Learnable Long-range Graph Transformer for Traffic Flow Prediction
Hierarchical Text Classification Optimization via Structural Entropy and Singular Smoothing
A Survey of Graph Neural Networks in Real World: Imbalance, Noise, Privacy and OOD Challenges
Community Detection in Large-Scale Complex Networks via Structural Entropy Game
A Survey of AIOps in the Era of Large Language Models
Knowledge Distillation in Federated Learning: A Survey on Long Lasting Challenges and New Solutions
Recommender Systems Meet Large Language Model Agents: A Survey
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation With Pre-Training
RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry
AEGK: Aligned Entropic Graph Kernels Through Continuous-Time Quantum Walks
Towards Target Sequential Rules
Distributed training of large language models: A survey
Data clustering: a fundamental method in data science and management
Can Large Language Models Serve as Evaluators for Code Summarization?
A Parameter-Efficient Federated Framework for Streaming Time Series Anomaly Detection via Lightweight Adaptation
A Scalable Algorithm for Fair Influence Maximization with Unbiased Estimator
NSF POSE: Phase II: OpenAD: An Integrated Open-Source Ecosystem for Anomaly Detection
III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption
III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations
SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers
III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation
III: Small: Fusion of Heterogeneous Networks for Synergistic Knowledge Discovery
TC: Small: Robust Anonymization on Social Networks
Collaborative Research: G-SESAME Cloud: A Dynamically Scalable Collaboration Community for Biological Knowledge Discovery
III:Small:Privacy Preserving Data Publishing: A Second Look on Group based Anonymization