39 citations · 57 across the 30 of their papers we have counts for
34 papers
Sparsity in control problems: Recent trends and open challenges
Luca Ballotta, Geethu Joseph, Geethu Josephb +7
As in all fields of engineering, control engineers constantly face a dichotomy between achieving high performance and the limited availability of resources for sensing, actuation,…
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…
Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark
Ngoc N. Tran, Anwar Said, Waseem Abbas +2
While graph-based Android malware classifiers report strong benchmark accuracy of over 94%, their performance sharply decreases up to 45% when exposed to previously unseen variants…
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…
Walk Matrix-Based Upper Bounds on Generalized Cospectral Mates
Muhammad Raza, Mudassir Shabbir, Waseem Abbas
The problem of characterizing graphs determined by their spectrum (DS) or generalized spectrum (DGS) has been a longstanding topic of interest in spectral graph theory, originating…
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…
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…
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
Anwar Said, Mudassir Shabbir, Tyler Derr +2
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the…
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 \…
Controllability Backbone in Networks
Obaid Ullah Ahmad, Waseem Abbas, Mudassir Shabbir
This paper studies the controllability backbone problem in dynamical networks defined over graphs. The main idea of the controllability backbone is to identify a small subset of ed…
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…
Edge Augmentation with Controllability Constraints in Directed Laplacian Networks
Waseem Abbas, Mudassir Shabbir, Yasin Yazıcıoglu +1
In this paper, we study the maximum edge augmentation problem in directed Laplacian networks to improve their robustness while preserving lower bounds on their strong structural co…
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…
Interplay Between Resilience and Accuracy in Resilient Vector Consensus in Multi-Agent Networks
Waseem Abbas, Mudassir Shabbir, Jiani Li +1
In this paper, we study the relationship between resilience and accuracy in the resilient distributed multi-dimensional consensus problem. We consider a network of agents, each of…
Strong Structural Controllability of Diffusively Coupled Networks: Comparison of Bounds Based on Distances and Zero Forcing
Yasin Yazicioglu, Mudassir Shabbir, Waseem Abbas +1
We study the strong structural controllability (SSC) of diffusively coupled networks, where the external control inputs are injected to only some nodes, namely the leaders. For suc…
Resilient Distributed Diffusion in Networks with Adversaries
Jiani Li, Waseem Abbas, Xenofon Koutsoukos
In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states…
Improving Network Robustness through Edge Augmentation While Preserving Strong Structural Controllability
Waseem Abbas, Mudassir Shabbir, Hassan Jaleel +1
In this paper, we consider a network of agents with Laplacian dynamics, and study the problem of improving network robustness by adding a maximum number of edges within the network…
Resilient Distributed Vector Consensus Using Centerpoints
Waseem Abbas, Mudassir Shabbir, Jiani Li +1
In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms. A common approach to ac…
Estimating Descriptors for Large Graphs
Zohair Raza Hassan, Mudassir Shabbir, Imdadullah Khan +1
Embedding networks into a fixed dimensional feature space, while preserving its essential structural properties is a fundamental task in graph analytics. These feature vectors (gra…
On the Impacts of Redundancy, Diversity, and Trust in Resilient Distributed State Estimation
Aritra Mitra, Faiq Ghawash, Shreyas Sundaram +1
We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. Recent attempts to solve this problem impose stringent redundan…