Graph Pooling

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Geometric Flow enhanced Graph Coarsening
Sep 14, 2026Chaoqun Fei, Guoxuan Li, Tinglve Zhou +2Graph Neural Networks Graph Coarsening
Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
Aug 4, 2026Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore TraniGraph Neural Networks Graph Pooling
University of Pisa, Dept. of Computer Science, 56127 Pisa PI, Italy · National Research Council, 56124 Pisa PI, Italy
MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification
Jul 29, 2026Yang Zhang, Xiao Zhou, Jonathan Warrell +3Graph Neural Networks Graph Pooling
Hierarchical Pooling for Sheaf Neural Networks
Jun 18, 2026Dionisia Naddeo, Carlo Abate, Pietro Liò +2Graph Neural Networks Graph Coarsening
UiT The Arctic University of Norway · UiT The Arctic University of Norway NORCE Norwegian Research Centre AS
SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing
Jun 10, 2026Anton Firc, Vojtěch Staněk, Zbyněk Lička +2Efficient Neural Network Inference Graph Neural Networks
The Role of Node Features in Graph Pooling
May 7, 2026Jan von Pichowski, Alžbeta Hrabošová, Ingo Scholtes +1Graph Neural Networks Graph Pooling
Center for AI and Data Science University of Würzburg, Germany · Department of Computing Science Umeå University, Sweden
PUFFIN: Protein Unit Discovery with Functional Supervision
Apr 16, 2026Gökçe Uludoğan, Buse Giledereli, Elif Ozkirimli +1Graph Pooling
Department of Computer Engineering Boğaziçi University Istanbul, Türkiye · Roche Informatics F. Hoffmann-La Roche AG Basel, Switzerland
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Jun 8, 2023Yuxiang Zhang, Chengguo Yuan, Xiao Wang +2Event-Based Vision Graph Neural Networks
aSchool of Computer Science and Technology, Anhui University, Hefei 230601, China.
NervePool: A Simplicial Pooling Layer
May 10, 2023Sarah McGuire Scullen, Ernst Röell, Elizabeth Munch +2Graph Coarsening Graph Pooling
Pacific Northwest National Lab, USA · Helmholtz Munich and Technical University of Munich, Germany · Michigan State University, USA +1

Sep 14, 2026, cs.AIJ/K move · Enter open · S save

Geometric Flow enhanced Graph Coarsening

Chaoqun Fei, Guoxuan Li, Tinglve Zhou, Chuanqing Wang+1

Graph Neural Networks Graph Coarsening Graph Pooling Clustering Graph Clustering

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Recently, researchers have proposed a graph pooling operation, akin to the pooling process in conventional convolutional neural networks (CNN), aimed at reducing the computation cost of Graph convolutional neural networks (GCNNs). While most GCNN-based methods treat graph pooling as a node clustering problem and propose learning a cluster assignment matrix, existing clustering-based pooling methods tend to focus solely on the rough topology information of graphs, neglecting the exploitation of higher-order mutual connections among neighbors. In terms of message passing on graph, the ease of information passing on edges reflects the closeness between neighboring nodes, which significantly relies on the interconnectivity among neighbors. In this study, we address this gap by considering such local connection information and introducing a novel graph pooling method named RicciPool. We introduce discrete graph curvature, particularly Ollivier-Ricci curvature, as a measure of higher-order connectivity around an edge. Subsequently, we construct an Ollivier-Ricci flow formula to reweigh edge weights, leveraging the crucial information provided by Ricci curvature, particularly vital for extracting clusters in graphs. Building upon this foundation, we utilize the spectral clustering technique to learn a new cluster assignment matrix. Experimental results on multiple bioinformatics protein datasets and social networks underscore the effectiveness of our proposed method.

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