11 citations · 11 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory
Anwar Said, Obaid Ullah Ahmad, Waseem Abbas +2
Network control theory (NCT) offers a robust analytical framework for understanding the influence of network topology on dynamic behaviors, enabling researchers to decipher how cer…
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir +2
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introdu…