6 papers
HyperEF 2.0: Spectral Hypergraph Coarsening via Krylov Subspace Expansion and Resistance-based Local Clustering
Hamed Sajadinia, Zhuo Feng
This paper introduces HyperEF 2.0, a scalable framework for spectral coarsening and clustering of large-scale hypergraphs through hyperedge effective resistances, aiming to decompo…
dyGRASS: Dynamic Spectral Graph Sparsification via Localized Random Walks on GPUs
Yihang Yuan, Ali Aghdaei, Zhuo Feng
This work presents dyGRASS, an efficient dynamic algorithm for spectral sparsification of large undirected graphs that undergo streaming edge insertions and deletions. At its core,…
SALMAN: Stability Analysis of Language Models Through the Maps Between Graph-based Manifolds
Wuxinlin Cheng, Yupeng Cao, Jinwen Wu +3
Recent strides in pretrained transformer-based language models have propelled state-of-the-art performance in numerous NLP tasks. Yet, as these models grow in size and deployment,…
A Spectral Framework for Evaluating Geodesic Distances Between Graphs
Soumen Sikder Shuvo, Ali Aghdaei, Zhuo Feng
This paper presents a spectral framework for quantifying the differentiation between graph data samples by introducing a novel metric named Graph Geodesic Distance (GGD). For two d…
SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning
Hamed Sajadinia, Ali Aghdaei, Zhuo Feng
State-of-the-art hypergraph partitioners utilize a multilevel paradigm to construct progressively coarser hypergraphs across multiple layers, guiding cut refinements at each level…
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds
Wuxinlin Cheng, Chenhui Deng, Ali Aghdaei +2
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 perform…