11 papers
Multi-Agent System Search via Active Substructure-aware Policy Optimization
Beicheng Xu, Bowen Fan, Weitong Qian +2
LLMs enable multi-agent systems (MAS) to tackle complex tasks, but manually designing agent roles, prompts, and communication structures requires substantial expertise and effort.…
AutoSci: A Memory-Centric Agentic System for the Full Scientific Research Lifecycle
Weitong Qian, Beicheng Xu, Zhongao Xie +16
Scientific research has traditionally been human-intensive, requiring researchers to coordinate literature, ideas, experiments, manuscripts, and review responses across long projec…
OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph
Chenxi Wan, Xunkai Li, Yilong Zuo +6
Multimodal-Attributed Graph (MAG) learning has achieved remarkable success in modeling complex real-world systems by integrating graph topology with rich attributes from multiple m…
OpenDDI: A Comprehensive Benchmark for DDI Prediction
Xinmo Jin, Bowen Fan, Xunkai Li +9
Drug-Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety. As experimental discovery is resource-intensive and time-consuming, efficient computa…
MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
Xunkai Li, Yuming Ai, Yinlin Zhu +7
Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures. While centraliz…
Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
Bowen Fan, Zhilin Guo, Xunkai Li +5
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social network analysis to molecular che…
Federated Graph Unlearning
Yuming Ai, Xunkai Li, Jiaqi Chao +5
The demand for data privacy has led to the development of frameworks like Federated Graph Learning (FGL), which facilitate decentralized model training. However, a significant oper…
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
Zhengyu Wu, Boyang Pang, Xunkai Li +6
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the…
LLaTA: Unlocking Graph Structure Learning with Tree-Guided Large Language Models
Zhihan Zhang, Xunkai Li, Lei Zhu +6
The emergence of large language models (LLMs) has popularized text-attributed graphs (TAGs), creating an urgent need for graph structure learning (GSL) methods that effectively lev…
Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
Xunkai Li, Bowen Fan, Zhengyu Wu +3
Machine unlearning, as a pivotal technology for enhancing model robustness and data privacy, has garnered significant attention in prevalent web mining applications, especially in…
OpenGU: A Comprehensive Benchmark for Graph Unlearning
Bowen Fan, Yuming Ai, Xunkai Li +3
Graph Machine Learning is essential for understanding and analyzing relational data. However, privacy-sensitive applications demand the ability to efficiently remove sensitive info…