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cs.LG2026
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…
cs.AI2026
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…
cs.SI2026
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…