1 citations · 1 across the 17 of their papers we have counts for
20 papers
Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective
Yuanyuan Xu, Wenjie Zhang, Yin Chen +2
Large language model (LLM)-based agents are increasingly becoming self-evolving systems that persist across interactions, maintain memories, use tools, acquire skills, refine workf…
Algebraic Subgraph Counting
Qiuyu Guo, Jianye Yang, Wenjie Zhang +3
Subgraph isomorphism counting is a fundamental problem in graph analytics, which aims to find the number of subgraph isomorphisms of a query graph in a data graph. The candidate tr…
HyperJoin: LLM-augmented Hypergraph Link Prediction for Joinable Table Discovery
Shiyuan Liu, Jianwei Wang, Xuemin Lin +3
As a pivotal task in data lake management, joinable table discovery has attracted widespread interest. While existing language model-based methods achieve remarkable performance by…
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…
Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications
Guangxin Su, Hanchen Wang, Jianwei Wang +3
Large Language Models (LLMs) have achieved remarkable success in natural language processing through strong semantic understanding and generation. However, their black-box nature l…
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…
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…
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…
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…
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…
TCGU: Data-centric Graph Unlearning based on Transferable Condensation
Fan Li, Xiaoyang Wang, Dawei Cheng +3
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 attenti…
Efficient Influence Minimization via Node Blocking
Jinghao Wang, Yanping Wu, Xiaoyang Wang +4
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 spre…
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Fan Li, Xiaoyang Wang, Dawei Cheng +3
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly…
Deep Overlapping Community Search via Subspace Embedding
Qing Sima, Jianke Yu, Xiaoyang Wang +3
Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categorie…
Efficient Unsupervised Community Search with Pre-trained Graph Transformer
Jianwei Wang, Kai Wang, Xuemin Lin +2
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 le…
Neural Attributed Community Search at Billion Scale
Jianwei Wang, Kai Wang, Xuemin Lin +2
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 bas…