160 citations · 272 across the 18 of their papers we have counts for
8 papers · 1 filter
UniCom: Towards a Unified and Cohesiveness-aware Framework for Community Search and Detection
Yifan Zhu, Hanchen Wang, Wenjie Zhang +2
Searching and detecting communities in real-world graphs underpins a wide range of applications. Despite the success achieved, current learning-based solutions regard community sea…
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…
HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
Han Chen, Hanchen Wang, Hongmei Chen +3
The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarchical structure of software. Existing…
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…
WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Shaozhen Ma, Hanchen Wang, Dong Wen +3
Overlapping community detection (OCD) is a fundamental graph data analysis task for extracting graph patterns. Traditional OCD methods can be broadly divided into node clustering a…
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…
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…
AI-Empowered Catalyst Discovery: A Survey from Classical Machine Learning Approaches to Large Language Models
Yuanyuan Xu, Hanchen Wang, Wenjie Zhang +6
Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds…