collaborators

9 papers

math.CO2026

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…

math.CO2026

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…

cs.CR2026

Quantifying the Generalization Gap: A New Benchmark for Out-of-Distribution Graph-Based Android Malware Classification

Ngoc N. Tran, Anwar Said, Waseem Abbas +2

While graph-based Android malware classifiers achieve over 94% accuracy on standard benchmarks, they exhibit a significant generalization gap under distribution shift, suffering up…

cs.LG2025

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 \…

cs.LG2025

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…

cs.LG2025

Robust Anomaly Detection with Graph Neural Networks using Controllability

Yifan Wei, Anwar Said, Waseem Abbas +1

Anomaly detection in complex domains poses significant challenges due to the need for extensive labeled data and the inherently imbalanced nature of anomalous versus benign samples…