6 papers
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,…
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…
inGRASS: Incremental Graph Spectral Sparsification via Low-Resistance-Diameter Decomposition
Ali Aghdaei, Zhuo Feng
This work presents inGRASS, a novel algorithm designed for incremental spectral sparsification of large undirected graphs. The proposed inGRASS algorithm is highly scalable and par…
SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks
John Anticev, Ali Aghdaei, Wuxinlin Cheng +1
SGM-PINN is a graph-based importance sampling framework to improve the training efficacy of Physics-Informed Neural Networks (PINNs) on parameterized problems. By applying a graph…