160 citations · 272 across the 18 of their papers we have counts for
20 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…
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…
GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model
Shaozhen Ma, Wei Huang, Hanchen Wang +2
Conditional generative models, particularly diffusion-based methods, have recently been applied to graph prediction by modeling the target as a conditional distribution given the i…
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…
Small Shifts, Large Gains: Unlocking Traditional TSP Heuristic Guided-Sampling via Unsupervised Neural Instance Modification
Wei Huang, Hanchen Wang, Dong Wen +1
The Traveling Salesman Problem (TSP) is one of the most representative NP-hard problems in route planning and a long-standing benchmark in combinatorial optimization. Traditional h…
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…
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…
STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model
Lincan Li, Hanchen Wang, Wenjie Zhang +1
Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-stationary, leading to the continuous challenge of spatial-temporal graph learning. In the past…
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching
Hanchen Wang, Ying Zhang, Lu Qin +3
Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in…
Pre-training Molecular Graph Representation with 3D Geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu +3
Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically modeled by their 2D topological structures, b…
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
Hanchen Wang, Defu Lian, Ying Zhang +2
Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging w…