6 papers
Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
Sen Zhao, Yifan Guan, Jinyuan Ni +6
Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes. These hyperedges adapt to…
Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball
Sen Zhao, Gaojie Xu, Shuyin Xia +4
Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation. Previous works focus primari…
Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction
Sen Zhao, Cheng Liu, Shuyin Xia +4
Link prediction aims to identify potential or future connections within a given graph structure. Position information is essential for link prediction, as it distinguishes homogene…
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…
GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data
Yewang Chen, Junfeng Li, Shuyin Xia +6
To effectively handle clustering task for large-scale datasets, we propose a novel scalable skeleton clustering algorithm, namely GBSK, which leverages the granular-ball technique…
FedRE: Robust and Effective Federated Learning with Privacy Preference
Tianzhe Xiao, Yichen Li, Yu Zhou +6
Despite Federated Learning (FL) employing gradient aggregation at the server for distributed training to prevent the privacy leakage of raw data, private information can still be d…