Search Results for author:
Found 7 papers, 5 papers with code
Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
In this work we propose HOGA, a novel attention-based model for learning circuit representations in a scalable and generalizable manner.
Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
To enable the base model permutation equivariant, we integrate it with graph topology and node features separately, resulting in local and global equivariant attention models.
Ranked #1 on
Node Classification
on pokec
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.
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.
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.
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.
