1 citations · 1 across the 8 of their papers we have counts for
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Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
Guan Wang, Shuyin Xia, Lei Qian +4
Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still face…
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
Guannan Lai, Yihui Feng, Xin Yang +5
Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. However, most existing articles attempt to address chal…
Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
Shuyin Xia, Xinjun Ma, Zhiyuan Liu +3
Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph c…
A robust three-way classifier with shadowed granular-balls based on justifiable granularity
Jie Yang, Lingyun Xiaodiao, Guoyin Wang +4
The granular-ball (GB)-based classifier introduced by Xia, exhibits adaptability in creating coarse-grained information granules for input, thereby enhancing its generality and fle…