33 citations · 34 across the 10 of their papers we have counts for
10 papers
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…
Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering
Xiangjun Zai, Xingyu Tan, Chen Chen +2
Long-document question answering (QA) requires large language models (LLMs) to reason over evidence scattered across lengthy documents, where answers often depend on event order, s…
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
Zhishuai Guo, Wenhan Wu, Chen Chen +3
Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot share raw data due to priva…
Universal Graph Backdoor Defense: A Feature-based Homophily Perspective
Mengting Pan, Fan Li, Chen Chen +1
Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) poses a significant barrier to b…
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…
Efficient Temporal Simple Path Graph Generation
Zhiyang Tang, Yanping Wu, Xiangjun Zai +3
Interactions between two entities often occur at specific timestamps, which can be modeled as a temporal graph. Exploring the relationships between vertices based on temporal paths…
Parallel Higher-order Truss Decomposition
Chen Chen, Jingya Qian, Hui Luo +2
The k-truss model is one of the most important models in cohesive subgraph analysis. The k-truss decomposition problem is to compute the trussness of each edge in a given graph, an…
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 Top-k Vulnerable Nodes Detection in Uncertain Graphs
Dawei Cheng, Chen Chen, Xiaoyang Wang +1
Uncertain graphs have been widely used to model complex linked data in many real-world applications, such as guaranteed-loan networks and power grids, where a node or edge may be a…
Critical Edge Identification: A K-Truss Based Model
Wenjie Zhu, Mengqi Zhang, Chen Chen +3
In a social network, the strength of relationships between users can significantly affect the stability of the network. In this paper, we use the k-truss model to measure the stabi…