5 papers · 1 filter
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…
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…
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…
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…
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…