73 citations · 115 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…