3 citations · 3 across the 4 of their papers we have counts for
10 papers · 1 filter
RAD: Rule-Augmented Relational Anomaly Detection
Noah Dahle, Anne Tumlin, Ngoc Tran +2
Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattenin…
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…
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…
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…