1 citations · 3 across the 7 of their papers we have counts for
7 papers
Affirmative Results on a Conjecture on the Column Space of the Adjacency Matrix
S. Akansha, K. C. Sivakumar
The Akbari-Cameron-Khosrovshahi (ACK) conjecture, which appears to be unresolved, states that for any simple graph with at least one edge, there exists a nonzero {}-ve…
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification
S. Akansha
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting outcomes in graph-structured data. However, a notable limitation of GNNs is their inability to provide ro…
Exploring Chebyshev Polynomial Approximations: Error Estimates for Functions of Bounded Variation
S Akansha
Approximation theory plays a central role in numerical analysis, undergoing continuous evolution through a spectrum of methodologies. Notably, Lebesgue, Weierstrass, Fourier, and C…
Over-Squashing in Graph Neural Networks: A Comprehensive survey
Singh Akansha
Graph Neural Networks (GNNs) revolutionize machine learning for graph-structured data, effectively capturing complex relationships. They disseminate information through interconnec…
Addressing the Impact of Localized Training Data in Graph Neural Networks
Akansha A
Graph Neural Networks (GNNs) have achieved notable success in learning from graph-structured data, owing to their ability to capture intricate dependencies and relationships betwee…
Adaptive Padé-Chebyshev Type Approximation of Piecewise Smooth Functions
S. Akansha, S. Baskar
A piecewise Padé-Chebyshev type (PiPCT) approximation method is proposed to minimize the Gibbs phenomenon in approximating piecewise smooth functions. A theorem on -error esti…
Adaptive Padé-Chebyshev Type Approximation to Piecewise Smooth Functions
S. Akansha, S. Baskar
The aim of this article is to study the role of piecewise implementation of Padé-Chebyshev type approximation in minimising Gibbs phenomena in approximating piecewise smooth functi…