7 papers
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…
RLMiner: Finding the Most Frequent k-sized Subgraph via Reinforcement Learning
Wei Huang, Hanchen Wang, Dong Wen +4
Identifying the most frequent induced subgraph of size in a target graph is a fundamental graph mining problem with direct implications for Web-related data mining and social n…
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…
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…
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…
RIDA: A Robust Attack Framework on Incomplete Graphs
Jianke Yu, Hanchen Wang, Chen Chen +5
Graph Neural Networks (GNNs) are vital in data science but are increasingly susceptible to adversarial attacks. To help researchers develop more robust GNN models, it's essential t…