3 citations · 5 across the 8 of their papers we have counts for
9 papers
HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning
Han Chen, Hanchen Wang, Hongmei Chen +3
Concept drift, driven by the rapid evolution of Android malware, severely degrades the performance of machine learning detectors. Current adaptation strategies are often reactive,…
ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation
Siyuan Zhang, Hanchen Wang, Dong Wen +2
Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex reasoning paths of multi-hop…
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…
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…
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…
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning
Guangxin Su, Yifan Zhu, Wenjie Zhang +2
Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art meth…
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…