39 citations · 57 across the 30 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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 \…
Computing Graph Descriptors on Edge Streams
Zohair Raza Hassan, Sarwan Ali, Imdadullah Khan +2
Feature extraction is an essential task in graph analytics. These feature vectors, called graph descriptors, are used in downstream vector-space-based graph analysis models. This i…
Byzantine Resilient Distributed Multi-Task Learning
Jiani Li, Waseem Abbas, Xenofon Koutsoukos
Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models sim…