19 citations · 20 across the 73 of their papers we have counts for
13 papers · 1 filter
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
Xunkai Li, Zhengyu Wu, Jiayi Wu +4
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration…
Towards Data-centric Machine Learning on Directed Graphs: a Survey
Henan Sun, Xunkai Li, Daohan Su +3
In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which…
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
Guochen Yan, Xunkai Li, Luyuan Xie +3
Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distribu…
DiRW: Path-Aware Digraph Learning for Heterophily
Daohan Su, Xunkai Li, Zhenjun Li +3
Recently, graph neural network (GNN) has emerged as a powerful representation learning tool for graph-structured data. However, most approaches are tailored for undirected graphs,…
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
Xunkai Li, Yinlin Zhu, Boyang Pang +7
Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inher…
Internal Consistency and Self-Feedback in Large Language Models: A Survey
Xun Liang, Shichao Song, Zifan Zheng +8
Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations. To address these, studies prefixed with "Self-" such as Self-Consistency, Self-Improve, a…
Acceleration Algorithms in GNNs: A Survey
Lu Ma, Zeang Sheng, Xunkai Li +5
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks. However, their inefficiency in training and inference presents challenges for scaling up…
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Yinlin Zhu, Xunkai Li, Zhengyu Wu +3
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfor…
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Xunkai Li, Meihao Liao, Zhengyu Wu +4
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and…
Rethinking Node-wise Propagation for Large-scale Graph Learning
Xunkai Li, Jingyuan Ma, Zhengyu Wu +4
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale…
Towards Effective and General Graph Unlearning via Mutual Evolution
Xunkai Li, Yulin Zhao, Zhengyu Wu +3
With the rapid advancement of AI applications, the growing needs for data privacy and model robustness have highlighted the importance of machine unlearning, especially in thriving…
FedGTA: Topology-aware Averaging for Federated Graph Learning
Xunkai Li, Zhengyu Wu, Wentao Zhang +3
Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs across multiple local systems. Existing FGL s…
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
Xunkai Li, Zhengyu Wu, Wentao Zhang +3
Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break dat…