73 citations · 115 across the 19 of their papers we have counts for
21 papers
Scalable Exact Densest P-Partite Subgraph Search in Heterogeneous Information Networks
Jiadong Xie, Jiaming Yang, Kangfei Zhao +1
Heterogeneous information networks (HINs) model typed entities and typed relations, where dense cross-type structures can reveal cohesive semantic patterns such as prolific author-…
Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm
Nan Hou, Kangfei Zhao, Jiadong Xie +1
Large language models (LLMs) are increasingly used for semantic query processing over large corpora. A set of semantic operators derived from relational algebra has been proposed t…
ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Chaohao Yuan, Zhenjie Song, Ercan Engin Kuruoglu +5
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the ove…
From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media
Tian Ma, Kaiyu Feng, Yu Rong +1
Personality prediction from social media posts is a critical task that implies diverse applications in psychology and sociology. The Myers Briggs Type Indicator (MBTI), a popular p…
PLACE: Prompt Learning for Attributed Community Search in Large Graphs
Shuheng Fang, Kangfei Zhao, Rener Zhang +2
In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural La…
Can Large Language Models Be Query Optimizer for Relational Databases?
Jie Tan, Kangfei Zhao, Rui Li +6
Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in dat…
A Survey of Graph Transformers: Architectures, Theories and Applications
Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu +6
Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-sm…
Natural Language-Assisted Multi-modal Medication Recommendation
Jie Tan, Yu Rong, Kangfei Zhao +5
Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patie…
CardOOD: Robust Query-driven Cardinality Estimation under Out-of-Distribution
Rui Li, Kangfei Zhao, Jeffrey Xu Yu +1
Query-driven learned estimators are accurate, flexible, and lightweight alternatives to traditional estimators in query optimization. However, existing query-driven approaches stru…
All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Shuheng Fang, Kangfei Zhao, Yu Rong +2
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations amon…
A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
Jianheng Tang, Kangfei Zhao, Jia Li
Entity alignment is the task of identifying corresponding entities across different knowledge graphs (KGs). Although recent embedding-based entity alignment methods have shown sign…
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
Jianheng Tang, Weiqi Zhang, Jiajin Li +3
Graph alignment, which aims at identifying corresponding entities across multiple networks, has been widely applied in various domains. As the graphs to be aligned are usually cons…
Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network
Yiqiang Yi, Xu Wan, Kangfei Zhao +2
Binding affinity prediction of three-dimensional (3D) protein ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches transform a 3D pro…
Transformer for Graphs: An Overview from Architecture Perspective
Erxue Min, Runfa Chen, Yatao Bian +7
Recently, Transformer model, which has achieved great success in many artificial intelligence fields, has demonstrated its great potential in modeling graph-structured data. Till n…
Community Search: A Meta-Learning Approach
Shuheng Fang, Kangfei Zhao, Guanghua Li +1
Community Search (CS) is one of the fundamental graph analysis tasks, which is a building block of various real applications. Given any query nodes, CS aims to find cohesive subgra…
Uncertainty-aware Cardinality Estimation by Neural Network Gaussian Process
Kangfei Zhao, Jeffrey Xu Yu, Zongyan He +1
Deep Learning (DL) has achieved great success in many real applications. Despite its success, there are some main problems when deploying advanced DL models in database systems, su…
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed
Yuli Jiang, Yu Rong, Hong Cheng +3
Given one or more query vertices, Community Search (CS) aims to find densely intra-connected and loosely inter-connected structures containing query vertices. Attributed Community…
Towards Expectation-Maximization by SQL in RDBMS
Kangfei Zhao, Jeffrey Xu Yu, Yu Rong +2
Integrating machine learning techniques into RDBMSs is an important task since there are many real applications that require modeling (e.g., business intelligence, strategic analys…
Dirichlet Graph Variational Autoencoder
Jia Li, Tomasyu Yu, Jiajin Li +5
Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation…
Towards Feature-free TSP Solver Selection: A Deep Learning Approach
Kangfei Zhao, Shengcai Liu, Yu Rong +1
The Travelling Salesman Problem (TSP) is a classical NP-hard problem and has broad applications in many disciplines and industries. In a large scale location-based services system,…
Graph Ordering: Towards the Optimal by Learning
Kangfei Zhao, Yu Rong, Jeffrey Xu Yu +2
Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, link prediction, and community detection. These model…