7 papers
Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
Abdullah Shaik, Anwar Said
We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning. Unlike conventional graph neural netw…
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 \…
Defining and Benchmarking a Data-Centric Design Space for Brain Graph Construction
Qinwen Ge, Roza G. Bayrak, Anwar Said +3
The construction of brain graphs from functional Magnetic Resonance Imaging (fMRI) data plays a crucial role in enabling graph machine learning for neuroimaging. However, current p…
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…
PropEnc: A Property Encoder for Graph Neural Networks
Anwar Said, Waseem Abbas, Xenofon Koutsoukos
Graph machine learning, particularly using graph neural networks, heavily relies on node features. However, many real-world systems, such as social and biological networks, lack no…
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…