5 papers · 1 filter
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…
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…
HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering
Ali Aghdaei, Zhuo Feng
This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. M…
HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering
Ali Aghdaei, Zhiqiang Zhao, Zhuo Feng
Hypergraphs allow modeling problems with multi-way high-order relationships. However, the computational cost of most existing hypergraph-based algorithms can be heavily dependent u…