62 citations · 93 across the 19 of their papers we have counts for
23 papers
Common-Neighbor-Count-Based Representative Possible World Finding on Uncertain Graphs
Chengjie Gu, Xiaoliang Xu, Yuxiang Wang +5
A representative possible world (RPW) is a deterministic graph derived from an uncertain graph where a designated structural feature closely approximates its expected…
VikingMem: A Memory Base Management System for Stateful LLM-based Applications
Jiajie Fu, Junwen Chen, Mengzhao Wang +5
Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-t…
Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views
Tingyang Chen, Cong Fu, Jiahua Wu +6
Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from…
Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization
Junhao Zhu, Lu Chen, Xiangyu Ke +4
Multi-modal analytical processing has the potential to transform applications in e-commerce, healthcare, entertainment, and beyond. However, real-world adoption remains elusive due…
Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search
Zhonggen Li, Xiangyu Ke, Yifan Zhu +3
Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficie…
Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Mengzhao Wang, Boyu Tan, Yunjun Gao +5
Hybrid search, the integration of lexical and semantic retrieval, has become a cornerstone of modern information retrieval systems, driven by demanding applications like Retrieval-…
OneDB: A Distributed Multi-Metric Data Similarity Search System
Tang Qian, Yifan Zhu, Lu Chen +5
Increasingly massive volumes of multi-modal data are being accumulated in many {real world} settings, including in health care and e-commerce. This development calls for effective…
Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities
Jiancheng Ruan, Tingyang Chen, Renchi Yang +2
Approximate Nearest Neighbor Search (ANNS) in high-dimensional spaces finds extensive applications in databases, information retrieval, recommender systems, etc. While graph-based…
Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration
Zhonggen Li, Xiangyu Ke, Yifan Zhu +2
Graph embeddings map graph nodes to continuous vectors and are foundational to community detection, recommendation, and many scientific applications. At billion-scale, however, exi…
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search
Tingyang Chen, Cong Fu, Xiangyu Ke +3
Maximum Inner Product Search (MIPS) is a fundamental challenge in machine learning and information retrieval, particularly in high-dimensional data applications. Existing approache…
Maximum Inner Product is Query-Scaled Nearest Neighbor
Tingyang Chen, Cong Fu, Kun Wang +5
Maximum Inner Product Search (MIPS) for high-dimensional vectors is pivotal across databases, information retrieval, and artificial intelligence. Existing methods either reduce MIP…
Accelerating Graph Indexing for ANNS on Modern CPUs
Mengzhao Wang, Haotian Wu, Xiangyu Ke +3
In high-dimensional vector spaces, Approximate Nearest Neighbor Search (ANNS) is a key component in database and artificial intelligence infrastructures. Graph-based methods, parti…
HC-SpMM: Accelerating Sparse Matrix-Matrix Multiplication for Graphs with Hybrid GPU Cores
Zhonggen Li, Xiangyu Ke, Yifan Zhu +2
Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges…
Spectral Subspace Clustering for Attributed Graphs
Xiaoyang Lin, Renchi Yang, Haoran Zheng +1
Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domai…
A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs
Zhicheng Liang, Yu Yang, Xiangyu Ke +2
Recent years have witnessed a growing trend toward employing deep reinforcement learning (Deep-RL) to derive heuristics for combinatorial optimization (CO) problems on graphs. Maxi…
An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models
Mengzhao Wang, Haotian Wu, Xiangyu Ke +3
Retrieval-augmented Large Language Models (LLMs) have reshaped traditional query-answering systems, offering unparalleled user experiences. However, existing retrieval techniques o…
GTS: GPU-based Tree Index for Fast Similarity Search
Yifan Zhu, Ruiyao Ma, Baihua Zheng +3
Similarity search, the task of identifying objects most similar to a given query object under a specific metric, has gathered significant attention due to its practical application…
Accelerating Biclique Counting on GPU
Linshan Qiu, Zhonggen Li, Xiangyu Ke +2
Counting (p,q)-bicliques in bipartite graphs poses a foundational challenge with broad applications, from densest subgraph discovery in algorithmic research to personalized content…
Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment
Mengzhao Wang, Weizhi Xu, Xiaomeng Yi +7
High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool for various data science and AI applications. As vector data scales up, in-memory indexes…
Scalable Community Search with Accuracy Guarantee on Attributed Graphs
Yuxiang Wang, Shuzhan Ye, Xiaoliang Xu +4
Given an attributed graph and a query node , \underline{C}ommunity \underline{S}earch over \underline{A}ttributed \underline{G}raphs (CS-AG) aims to find a structure- and at…
GPU-Accelerated Batch-Dynamic Subgraph Matching
Linshan Qiu, Lu Chen, Hailiang Jie +4
Subgraph matching has garnered increasing attention for its diverse real-world applications. Given the dynamic nature of real-world graphs, addressing evolving scenarios without in…
View-based Explanations for Graph Neural Networks
Tingyang Chen, Dazhuo Qiu, Yinghui Wu +3
Generating explanations for graph neural networks (GNNs) has been studied to understand their behavior in analytical tasks such as graph classification. Existing approaches aim to…
MUST: An Effective and Scalable Framework for Multimodal Search of Target Modality
Mengzhao Wang, Xiangyu Ke, Xiaoliang Xu +4
We investigate the problem of multimodal search of target modality, where the task involves enhancing a query in a specific target modality by integrating information from auxiliar…