16 citations · 42 across the 25 of their papers we have counts for
14 papers · 1 filter
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
Guochen Yan, Luyuan Xie, Xinyi Gao +4
Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distr…
Training-free Heterogeneous Graph Condensation via Data Selection
Yuxuan Liang, Wentao Zhang, Xinyi Gao +5
Efficient training of large-scale heterogeneous graphs is of paramount importance in real-world applications. However, existing approaches typically explore simplified models to mi…
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
Xinyi Gao, Yayong Li, Tong Chen +3
With the increasing computation of training graph neural networks (GNNs) on large-scale graphs, graph condensation (GC) has emerged as a promising solution to synthesize a compact,…
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
Yunhui Liu, Xinyi Gao, Tieke He +2
Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate rel…
Epidemiology-informed Network for Robust Rumor Detection
Wei Jiang, Tong Chen, Xinyi Gao +3
The rapid spread of rumors on social media has posed significant challenges to maintaining public trust and information integrity. Since an information cascade process is essential…
HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
Qianru Zhang, Xinyi Gao, Haixin Wang +3
Spatial-temporal graph representations play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime predictio…
ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems
Zongwei Wang, Min Gao, Junliang Yu +4
Recent advances in ID-free recommender systems have attracted significant attention for effectively addressing the cold start problem. However, their vulnerability to malicious att…
Reliable Node Similarity Matrix Guided Contrastive Graph Clustering
Yunhui Liu, Xinyi Gao, Tieke He +3
Graph clustering, which involves the partitioning of nodes within a graph into disjoint clusters, holds significant importance for numerous subsequent applications. Recently, contr…
Graph Condensation for Open-World Graph Learning
Xinyi Gao, Tong Chen, Wentao Zhang +3
The burgeoning volume of graph data presents significant computational challenges in training graph neural networks (GNNs), critically impeding their efficiency in various applicat…
RobGC: Towards Robust Graph Condensation
Xinyi Gao, Hongzhi Yin, Tong Chen +3
Graph neural networks (GNNs) have attracted widespread attention for their impressive capability of graph representation learning. However, the increasing prevalence of large-scale…
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…
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
Xinyi Gao, Guanhua Ye, Tong Chen +3
The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements. In resp…
Challenging Low Homophily in Social Recommendation
Wei Jiang, Xinyi Gao, Guandong Xu +2
Social relations are leveraged to tackle the sparsity issue of user-item interaction data in recommendation under the assumption of social homophily. However, social recommendation…
Graph Condensation: A Survey
Xinyi Gao, Junliang Yu, Tong Chen +3
The rapid growth of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs). To address these challenges, gr…