19 citations · 19 across the 4 of their papers we have counts for
4 papers · 1 filter
Shapley-Value-Based Graph Sparsification for GNN Inference
Selahattin Akkas, Ariful Azad
Graph sparsification is a key technique for improving inference efficiency in Graph Neural Networks by removing edges with minimal impact on predictions. GNN explainability methods…
DistShap: Scalable GNN Explanations with Distributed Shapley Values
Selahattin Akkas, Aditya Devarakonda, Ariful Azad
With the growing adoption of graph neural networks (GNNs), explaining their predictions has become increasingly important. However, attributing predictions to specific edges or fea…
GNNShap: Scalable and Accurate GNN Explanation using Shapley Values
Selahattin Akkas, Ariful Azad
Graph neural networks (GNNs) are popular machine learning models for graphs with many applications across scientific domains. However, GNNs are considered black box models, and it…
Rank Position Forecasting in Car Racing
Bo Peng, Jiayu Li, Selahattin Akkas +4
Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the…