Explanipedia

Approximate Nearest Neighbor Search of Large Scale Vectors on Distributed Storage Open

Kun Yu, Jiabao Jin, Xiaoyao Zhong, Peng Cheng, Lei Chen , et al. · 2025

Approximate Nearest Neighbor Search (ANNS) in high-dimensional space is an essential operator in many online services, such as information retrieval and recommendation. Indices constructed by the state-of-the-art ANNS algorithms must be st…

Accelerating K-Core Computation in Temporal Graphs Open

Ma Zhuo, Dong Wen, Hanchen Wang, Wentao Li, Wenjie Zhang , et al. · 2025

We address the problem of enumerating all temporal k-cores given a query time range and a temporal graph, which suffers from poor efficiency and scalability in the state-of-the-art solution. Motivated by an existing concept called core tim…

DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learning Open

Li Fan, Xiaoyang Wang, Wenjie Zhang, Ying Zhang, Xuemin Lin · 2025

Deep graph models have achieved great success in network representation learning. However, their focus on pairwise relationships restricts their ability to learn pervasive higher-order interactions in real-world systems, which can be natur…

GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query Open

Ge Meng, Liping Wang, Xuemin Lin, Yuang Zhang, Kunming Wang · 2025

Trajectory mining has attracted significant attention. This paper addresses the Top-k Representative Similar Subtrajectory Query (TRSSQ) problem, which aims to find the k most representative subtrajectories similar to a query. Existing met…

Editorial for Special Issue: VLDB 2022 Open

Juliana Freire, Fatma Özcan, Xuemin Lin · 2025

Picking point localization method based on semantic reasoning for complex picking scenarios in vineyards Open

Xuemin Lin, Jinhai Wang, J. Wang, Huiling Wei, Mingyou Chen , et al. · 2025

Ranking on Dynamic Graphs: An Effective and Robust Band-Pass Disentangled Approach Open

Yingxuan Li, Yuanyuan Xu, Xuemin Lin, Wenjie Zhang, Ying Zhang · 2025

Covering K-Cliques in Billion-Scale Graphs Open

Kaiyu Chen, Dong Wen, Hanchen Wang, Zhengyi Yang, Wenjie Zhang , et al. · 2025

UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs Open

Yuanyuan Xu, Wenjie Zhang, Xuemin Lin, Ying Zhang · 2025

Dynamic graphs are formulated in continuous-time or discrete-time dynamic graphs. They differ in temporal granularity: Continuous-Time Dynamic Graphs (CTDGs) exhibit rapid, localized changes, while Discrete-Time Dynamic Graphs (DTDGs) show…

Infinite Stream Estimation under Personalized <i>w</i> -Event Privacy Open

Leilei Du, Cheng Peng, Lei Chen, Heng Tao Shen, Xuemin Lin , et al. · 2025

Streaming data collection is indispensable for stream data analysis, such as event monitoring. However, publishing these data directly leads to privacy leaks. w -event privacy is a valuable tool to protect individual privacy within a given…

Ensemble-based Deep Multilayer Community Search Open

Jianwei Wang, Yuehai Wang, Kai Wang, Xuemin Lin, Wenjie Zhang , et al. · 2025

Multilayer graphs, consisting of multiple interconnected layers, are widely used to model diverse relationships in the real world. A community is a cohesive subgraph that offers valuable insights for analyzing (multilayer) graphs. Recently…

On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing Open

Jianwei Wang, Kai Wang, Ying Zhang, Wenjie Zhang, Xiwei Xu , et al. · 2025

Missing data imputation, which aims to impute the missing values in the raw datasets to achieve the completeness of datasets, is crucial for modern data-driven models like large language models (LLMs) and has attracted increasing interest …

Common Neighborhood Estimation over Bipartite Graphs under Local Differential Privacy Open

Yizhang He, Kai Wang, Wenjie Zhang, Xuemin Lin, Ying Zhang · 2024

Bipartite graphs, formed by two vertex layers, arise as a natural fit for modeling the relationships between two groups of entities. In bipartite graphs, common neighborhood computation between two vertices on the same vertex layer is a ba…

Counting Butterflies over Streaming Bipartite Graphs with Duplicate Edges Open

Lingkai Meng, Long Yuan, Xuemin Lin, Chengjie Li, Kai Wang , et al. · 2024

Bipartite graphs are commonly used to model relationships between two distinct entities in real-world applications, such as user-product interactions, user-movie ratings and collaborations between authors and publications. A butterfly (a 2…

Efficient Dynamic Attributed Graph Generation Open

Fan Li, Xiaoyang Wang, Dawei Cheng, Cong Chen, Ying Zhang , et al. · 2024

Data generation is a fundamental research problem in data management due to its diverse use cases, ranging from testing database engines to data-specific applications. However, real-world entities often involve complex interactions that ca…

StructRide: A Framework to Exploit the Structure Information of Shareability Graph in Ridesharing Open

Jiexi Zhan, Yu Chen, Cheng Peng, Lei Chen, Wangze Ni , et al. · 2024

Ridesharing services play an essential role in modern transportation, which significantly reduces traffic congestion and exhaust pollution. In the ridesharing problem, improving the sharing rate between riders can not only save the travel …

Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery Open

Ruifeng Li, Wei Liu, Xiangxin Zhou, Meimingwei Li, Qiang Zhang , et al. · 2024

In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular property prediction. Existing methods for few-shot molecular prope…

TCGU: Data-centric Graph Unlearning based on Transferable Condensation Open

Li Fan, Xiaoyang Wang, Dawei Cheng, Wenjie Zhang, Ying Zhang , et al. · 2024

With growing demands for data privacy and model robustness, graph unlearning (GU), which erases the influence of specific data on trained GNN models, has gained significant attention. However, existing exact unlearning methods suffer from …

EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy Open

Yihong Huang, Yuang Zhang, Liping Wang, Fan Zhang, Xuemin Lin · 2024

Unsupervised Outlier Detection (UOD) is an important data mining task. With the advance of deep learning, deep Outlier Detection (OD) has received broad interest. Most deep UOD models are trained exclusively on clean datasets to learn the …

Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks Open

Yiqi Wang, Long Yuan, Wenjie Zhang, Xuemin Lin, Zi Chen , et al. · 2024

Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have …

Efficient Maximal Frequent Group Enumeration in Temporal Bipartite Graphs Open

Yanping Wu, Renjie Sun, Xiaoyang Wang, Dong Wen, Ying Zhang , et al. · 2024

Cohesive subgraph mining is a fundamental problem in bipartite graph analysis. In reality, relationships between two types of entities often occur at some specific timestamps, which can be modeled as a temporal bipartite graph. However, th…

Efficient Influence Minimization via Node Blocking Open

Jinghao Wang, Yanping Wu, Xiaoyang Wang, Ying Zhang, Lu Qin , et al. · 2024

Given a graph G, a budget k and a misinformation seed set S, Influence Minimization (IMIN) via node blocking aims to find a set of k nodes to be blocked such that the expected spread of S is minimized. This problem finds important applicat…

Deep Overlapping Community Search via Subspace Embedding Open

Qing Sima, Jianke Yu, Xiaoyang Wang, Wenjie Zhang, Ying Zhang , et al. · 2024

Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) m…

Hypergraph Self-supervised Learning with Sampling-efficient Signals Open

Li Fan, Xiaoyang Wang, Dawei Cheng, Wenjie Zhang, Ying Zhang , et al. · 2024

Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discr…

A Survey of Distributed Graph Algorithms on Massive Graphs Open

Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai, Peng Cheng , et al. · 2024

Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been…

Efficient Unsupervised Community Search with Pre-trained Graph Transformer Open

Jianwei Wang, Kai Wang, Xuemin Lin, Wenjie Zhang, Ying Zhang · 2024

Community search has aroused widespread interest in the past decades. Among existing solutions, the learning-based models exhibit outstanding performance in terms of accuracy by leveraging labels to 1) train the model for community score l…

Neural Attributed Community Search at Billion Scale Open

Jianwei Wang, Kai Wang, Xuemin Lin, Wenjie Zhang, Ying Zhang · 2024

Community search has been extensively studied in the past decades. In recent years, there is a growing interest in attributed community search that aims to identify a community based on both the query nodes and query attributes. A set of t…

Simple and deep graph attention networks Open

Guangxin Su, Hanchen Wang, Ying Zhang, Wenjie Zhang, Xuemin Lin · 2024

Graph Attention Networks (GATs) and Graph Convolutional Neural Networks (GCNs) are two state-of-the-art architectures in Graph Neural Networks (GNNs). It is well known that both models suffer from performance degradation when more GNN laye…

Wait to be Faster: a Smart Pooling Framework for Dynamic Ridesharing Open

Xiaoyao Zhong, Jiabao Jin, Peng Chen, Wangze Ni, Libin Zheng , et al. · 2024

Ridesharing services, such as Uber or Didi, have attracted considerable attention in recent years due to their positive impact on environmental protection and the economy. Existing studies require quick responses to orders, which lack the …

Diffusion-based Graph Generative Methods Open

Hongyang Chen, Can Xu, Lingyu Zheng, Qiang Zhang, Xuemin Lin · 2024

Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks. Among them, graph generation attracts significant research attention for its broad application in real life. In our surve…

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