3 citations · 7 across the 12 of their papers we have counts for
17 papers
RADE: Random Add-Drop Edge as a Regularizer
Danial Saber, Amirali Salehi-Abari
Graph Neural Networks (GNNs) suffer from overfitting and over-squashing of long-range information. Stochastic graph augmentations (e.g., edge deletion) regularize training against…
Over-Squashing in GNNs and Causal Inference of Rewiring Strategies
Danial Saber, Amirali Salehi-Abari
Graph neural networks (GNNs) have exhibited state-of-the-art performance across wide-range of domains such as recommender systems, material design, and drug repurposing. Yet messag…
Scalable Expressiveness through Preprocessed Graph Perturbations
Danial Saber, Amirali Salehi-Abari
Graph Neural Networks (GNNs) have emerged as the predominant method for analyzing graph-structured data. However, canonical GNNs have limited expressive power and generalization ca…
Stochastic Subgraph Neighborhood Pooling for Subgraph Classification
Shweta Ann Jacob, Paul Louis, Amirali Salehi-Abari
Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classificat…
PiXi: Password Inspiration by Exploring Information
Shengqian Wang, Amirali Salehi-Abari, Julie Thorpe
Passwords, a first line of defense against unauthorized access, must be secure and memorable. However, people often struggle to create secure passwords they can recall. To address…
Simplifying Subgraph Representation Learning for Scalable Link Prediction
Paul Louis, Shweta Ann Jacob, Amirali Salehi-Abari
Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs ar…