2 citations · 4 across the 15 of their papers we have counts for
17 papers
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…
Answer-then-Edit: Reasoning Skeleton Editing for Anti-Distillation with Preserved Utility
Fan Li, Mengting Pan, Sijia Xu +3
Proprietary large language models (LLMs) entail substantial intellectual and financial investment, making them valuable intellectual property (IP). However, even when deployed via…
Weaving Multi-Source Evidence for Biomedical Reasoning: The BioMedHop Benchmark and BioWeave Framework
Xingyu Tan, Shiyuan Liu, Xiaoyang Wang +5
Biomedical question answering (QA) increasingly requires reasoning over interacting entities, where supporting evidence is scattered across biomedical knowledge graphs, literature…
Anchor-guided Hypergraph Condensation with Dual-level Discrimination
Fan Li, Xiaoyang Wang, Chen Chen +1
The increasing prevalence of large-scale hypergraphs poses significant computational challenges for hypergraph neural network (HNN) training. To address this, hypergraph condensati…
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…
Beyond Homophily: Community Search on Heterophilic Graphs
Qing Sima, Xiaoyang Wang, Wenjie Zhang
Community search aims to identify a refined set of nodes that are most relevant to a given query, supporting tasks ranging from fraud detection to recommendation. Unlike homophilic…
HL-index: Fast Reachability Query in Hypergraphs
Peiting Xie, Xiangjun Zai, Yanping Wu +3
Reachability in hypergraphs is essential for modeling complex groupwise interactions in real-world applications such as co-authorship, social network, and biological analysis, wher…
Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
Zian Zhai, Fan Li, Xingyu Tan +2
Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data. However, a fundamental challen…
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning
Xingyu Tan, Xiaoyang Wang, Qing Liu +4
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge. Current hybrid RAG system retrieves evidence from both knowledge gra…
SGPT: Few-Shot Prompt Tuning for Signed Graphs
Zian Zhai, Sima Qing, Xiaoyang Wang +1
Signed Graph Neural Networks (SGNNs) are effective in learning expressive representations for signed graphs but typically require substantial task-specific labels, limiting their a…
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…
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…
Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
Xingyu Tan, Xiaoyang Wang, Qing Liu +3
Large Language Models (LLMs) have achieved impressive results in various tasks but struggle with hallucination problems and lack of relevant knowledge, especially in deep complex r…
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…
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…