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Found 4 papers, 2 papers with code
inGRASS: Incremental Graph Spectral Sparsification via Low-Resistance-Diameter Decomposition
This work presents inGRASS, a novel algorithm designed for incremental spectral sparsification of large undirected graphs.
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds
Modern graph neural networks (GNNs) can be sensitive to changes in the input graph structure and node features, potentially resulting in unpredictable behavior and degraded performance.
HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering
This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances.
HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering
To address the ever-increasing computational challenges, graph coarsening can be potentially applied for preprocessing a given hypergraph by aggressively aggregating its vertices (nodes).
