Search Results for author:
Found 21 papers, 6 papers with code
SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning
State-of-the-art hypergraph partitioners utilize a multilevel paradigm to construct progressively coarser hypergraphs across multiple layers, guiding cut refinements at each level of the hierarchy.
SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks
SGM-PINN is a graph-based importance sampling framework to improve the training efficacy of Physics-Informed Neural Networks (PINNs) on parameterized problems.
Geodesic Distance Between Graphs: A Spectral Metric for Assessing the Stability of Graph Neural Networks
This paper presents a spectral framework for assessing the generalization and stability of Graph Neural Networks (GNNs) by introducing a Graph Geodesic Distance (GGD) metric.
Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents
We show that users spend a lot of ``effort'' on these questions in terms of signals like clicks and session length, and that they are also challenging for GPT-4.
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.
A Topology-aware Graph Coarsening Framework for Continual Graph Learning
Continual learning on graphs tackles the problem of training a graph neural network (GNN) where graph data arrive in a streaming fashion and the model tends to forget knowledge from previous tasks when updating with new data.
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents.
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.
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data.
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks
In this paper, we propose GARNET, a scalable spectral method to boost the adversarial robustness of GNN models for both homophilic and heterophilic graphs.
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).
SGL: Spectral Graph Learning from Measurements
Through extensive experiments for a variety of real-world test cases, we show that the proposed approach is highly scalable for learning ultra-sparse resistor networks without sacrificing solution quality.
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model.
A Unified Spectral Sparsification Framework for Directed Graphs
For the first time, we prove the existence of linear-sized spectral sparsifiers for general directed graphs and introduce a practically-efficient and unified spectral graph sparsification approach that allows sparsifying real-world, large-scale directed and undirected graphs with guaranteed preservation of the original graph spectra.
Graph Learning via Spectral Densification
Graph learning plays important role in many data mining and machine learning tasks, such as manifold learning, data representation and analysis, dimensionality reduction, data clustering, and visualization, etc.
SF-GRASS: Solver-Free Graph Spectral Sparsification
Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sparse matrices, spectral partitioning of undirected graphs, vectorless verification of power/thermal grids, representation learning of large graphs, etc.
GRASPEL: Graph Spectral Learning at Scale
Learning meaningful graphs from data plays important roles in many data mining and machine learning tasks, such as data representation and analysis, dimension reduction, data clustering, and visualization, etc.
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information.
Similarity-Aware Spectral Sparsification by Edge Filtering
In recent years, spectral graph sparsification techniques that can compute ultra-sparse graph proxies have been extensively studied for accelerating various numerical and graph-related applications.
Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification
The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering.
