7 papers
An upper bound on generalized cospectral mates of oriented hraphs using skew-walk matrices
Muhammad Raza, Obaid Ullah Ahmed, Mudassir Shabbir +2
Let be an oriented graph with skew-adjacency matrix . Two oriented graphs and are said to share the same generalized skew spectrum if and have the s…
On the number of generalized cospectral mates of graphs
Muhammad Raza, Obaid Ullah Ahmad, Mudassir Shabbir +1
This paper establishes an upper bound on the number of generalized cospectral mates of simple graphs, where the generalized spectrum consists of the spectrum of a graph and its com…
A Survey of Graph Unlearning
Anwar Said, Ngoc N. Tran, Yuying Zhao +4
Graph unlearning emerges as a crucial advancement in the pursuit of responsible AI, providing the means to remove sensitive data traces from trained models, thereby upholding the \…
Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning
Anwar Said, Yifan Wei, Obaid Ullah Ahmad +3
In this article, we utilize the concept of average controllability in graphs, along with a novel rank encoding method, to enhance the performance of Graph Neural Networks (GNNs) in…
Walk Matrix-Based Upper Bounds on Generalized Cospectral Mates
Muhammad Raza, Mudassir Shabbir, Waseem Abbas
The problem of characterizing graphs determined by their spectrum (DS) or generalized spectrum (DGS) has been a longstanding topic of interest in spectral graph theory, originating…
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning
Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir +2
This paper introduces a novel framework for graph sparsification that preserves the essential learning attributes of original graphs, improving computational efficiency and reducin…