1 citations · 1 across the 7 of their papers we have counts for
7 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…
GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing
Shuyin Xia, Guan Wang, Gaojie Xu +2
The objective of graph coarsening is to generate smaller, more manageable graphs while preserving key information of the original graph. Previous work were mainly based on the pers…
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…
Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary
Yanhua Li, Xiaocao Ouyang, Chaofan Pan +6
Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unkno…
GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems
Shuyin Xia, Xinyu Lin, Guan Wang +4
Optimization problems aim to find the optimal solution, which is becoming increasingly complex and difficult to solve. Traditional evolutionary optimization methods always overlook…