1 citations · 2 across the 27 of their papers we have counts for
16 papers · 1 filter
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
Runze Li, Hanchen Wang, Wenjie Zhang +4
Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling le…
DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learning
Fan Li, Xiaoyang Wang, Wenjie Zhang +2
Deep graph models have achieved great success in network representation learning. However, their focus on pairwise relationships restricts their ability to learn pervasive higher-o…
Triangle Counting in Hypergraph Streams: A Complete and Practical Approach
Lingkai Meng, Long Yuan, Xuemin Lin +2
Triangle counting in hypergraph streams, including both hyper-vertex and hyper-edge triangles, is a fundamental problem in hypergraph analytics, with broad applications. However, e…
Accelerating Historical K-Core Search in Temporal Graphs
Zhuo Ma, Dong Wen, Kaiyu Chen +3
We study the temporal k-core component search (TCCS), which outputs the k-core containing the query vertex in the snapshot over an arbitrary query time window in a temporal graph.…
Accelerating K-Core Computation in Temporal Graphs
Zhuo Ma, Dong Wen, Hanchen Wang +3
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…
Empowering Tabular Data Preparation with Language Models: Why and How?
Mengshi Chen, Yuxiang Sun, Tengchao Li +5
Data preparation is a critical step in enhancing the usability of tabular data and thus boosts downstream data-driven tasks. Traditional methods often face challenges in capturing…
HiTeC: Hierarchical Contrastive Learning on Text-Attributed Hypergraph with Semantic-Aware Augmentation
Mengting Pan, Fan Li, Chen Chen +2
Contrastive learning (CL) has become a dominant paradigm for self-supervised hypergraph learning, enabling effective training without costly labels. However, node entities in real-…
Towards Unsupervised Training of Matching-based Graph Edit Distance Solver via Preference-aware GAN
Wei Huang, Hanchen Wang, Dong Wen +3
Graph Edit Distance (GED) is a fundamental graph similarity metric widely used in various applications. However, computing GED is an NP-hard problem. Recent state-of-the-art hybrid…
Efficient Computation of Hyper-triangles on Hypergraphs
Haozhe Yin, Kai Wang, Wenjie Zhang +3
Hypergraphs, which use hyperedges to capture groupwise interactions among different entities, have gained increasing attention recently for their versatility in effectively modelin…
Motif Counting in Complex Networks: A Comprehensive Survey
Haozhe Yin, Kai Wang, Wenjie Zhang +3
Motif counting plays a crucial role in understanding the structural properties of networks. By computing motif frequencies, researchers can draw key insights into the structural pr…
DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching
Wei Huang, Hanchen Wang, Dong Wen +3
The Graph Edit Distance (GED) problem, which aims to compute the minimum number of edit operations required to transform one graph into another, is a fundamental challenge in graph…
ProbDiffFlow: An Efficient Learning-Free Framework for Probabilistic Single-Image Optical Flow Estimation
Mo Zhou, Jianwei Wang, Xuanmeng Zhang +5
This paper studies optical flow estimation, a critical task in motion analysis with applications in autonomous navigation, action recognition, and film production. Traditional opti…
UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs
Yuanyuan Xu, Wenjie Zhang, Xuemin Lin +1
Dynamic graphs are formulated in continuous-time or discrete-time dynamic graphs. They differ in temporal granularity: Continuous-Time Dynamic Graphs (CTDGs) exhibit rapid, localiz…
Unlocking Multi-Modal Potentials for Link Prediction on Dynamic Text-Attributed Graphs
Yuanyuan Xu, Wenjie Zhang, Ying Zhang +2
Dynamic Text-Attributed Graphs (DyTAGs) are a novel graph paradigm that captures evolving temporal events (edges) alongside rich textual attributes. Existing studies can be broadly…
Ensemble-based Deep Multilayer Community Search
Jianwei Wang, Yuehai Wang, Kai Wang +3
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 va…
On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing
Jianwei Wang, Kai Wang, Ying Zhang +3
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 la…