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…
Rethinking Token Reduction for Large Vision-Language Models
Yi Wang, Haofei Zhang, Qihan Huang +7
Large Vision-Language Models (LVLMs) excel in visual understanding and reasoning, but the excessive visual tokens lead to high inference costs. Although recent token reduction meth…
SSTODE: Ocean-Atmosphere Physics-Informed Neural ODEs for Sea Surface Temperature Prediction
Zheng Jiang, Wei Wang, Gaowei Zhang +1
Sea Surface Temperature (SST) is crucial for understanding upper-ocean thermal dynamics and ocean-atmosphere interactions, which have profound economic and social impacts. While da…