433 citations · 1.3k across the 96 of their papers we have counts for
27 papers · 1 filter
Failure Localization Capability: Theorem Proof and Evaluation
Liang Ma, Ting He, Ananthram Swami +2
This is a technical report, containing all the theorem proofs and additional evaluations in paper "Network Capability in Localizing Node Failures via End-to-end Path Measurements"…
Link Identifiability with Two Monitors: Proof of Selected Theorems
Liang Ma, Ting He, Kin K. Leung +2
This is a technical report, containing all the theorem proofs in paper "Link Identifiability in Communication Networks with Two Monitors" by Liang Ma, Ting He, Kin K. Leung, Ananth…
Identification of Additive Link Metrics: Proof of Selected Theorems
Liang Ma, Ting He, Kin K. Leung +2
This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability o…
Additive Link Metrics Identification: Proof of Selected Lemmas and Propositions
Liang Ma, Ting He, Kin K. Leung +2
This is a technical report, containing all the lemma and proposition proofs in paper "Topological Constraints on Identifying Additive Link Metrics via End-to-end Paths Measurements…
Efficient Identification of Additive Link Metrics: Theorem Proof and Evaluations
Liang Ma, Ting He, Kin K. Leung +2
This is a technical report, containing all the theorem proofs and additional evaluations in paper "Efficient Identification of Additive Link Metrics via Network Tomography" by Lian…
Partial Network Identifiability: Theorem Proof and Evaluation
Liang Ma, Ting He, Kin K. Leung +2
This is a technical report, containing all the theorem proofs and additional evaluations in paper "Monitor Placement for Maximal Identifiability in Network Tomography" by Liang Ma,…
Fundamental Theories in Node Failure Localization
Liang Ma, Ting He, Ananthram Swami +3
This is a technical report, containing all the theorem proofs in paper "Node Failure Localization in Communication Networks via Network Tomography" by Liang Ma, Ting He, Ananthram…
Node Failure Localization: Theorem Proof
Liang Ma, Ting He, Ananthram Swami +2
This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Anan…
Efficient power allocation using graph neural networks and deep algorithm unfolding
Arindam Chowdhury, Gunjan Verma, Chirag Rao +2
We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we propose a hybrid neural architecture inspired by the algorithm…
Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks
Gojko Cutura, Boning Li, Ananthram Swami +1
We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evo…
Adaptive Contention Window Design using Deep Q-learning
Abhishek Kumar, Gunjan Verma, Chirag Rao +2
We study the problem of adaptive contention window (CW) design for random-access wireless networks. More precisely, our goal is to design an intelligent node that can dynamically a…
Resource Allocation in One-dimensional Distributed Service Networks with Applications
Nitish K. Panigrahy, Prithwish Basu, Philippe Nain +4
We consider assignment policies that allocate resources to users, where both resources and users are located on a one-dimensional line. First, we consider unidirectional assignment…
On the Analysis of Spatially Constrained Power of Two Choice Policies
Nitish K. Panigrahy, Prithwish Basu, Don Towsley +2
We consider a class of power of two choice based assignment policies for allocating users to servers, where both users and servers are located on a two-dimensional Euclidean plane.…
You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning
Shitong Zhu, Shasha Li, Zhongjie Wang +5
As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a spectrum of adversarial attacks have emerged with the goal of evading such middleboxes. Many of these att…
Distributed Scheduling using Graph Neural Networks
Zhongyuan Zhao, Gunjan Verma, Chirag Rao +2
A fundamental problem in the design of wireless networks is to efficiently schedule transmission in a distributed manner. The main challenge stems from the fact that optimal link s…
Unsupervised Joint -node Graph Representations with Compositional Energy-Based Models
Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami +1
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in…
An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks
Sumit Kumar Jha, Susmit Jha, Rickard Ewetz +4
Deep neural networks have been shown to be vulnerable to membership inference attacks wherein the attacker aims to detect whether specific input data were used to train the model.…
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
Arindam Chowdhury, Gunjan Verma, Chirag Rao +2
We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we depart from classical purely model-based approaches and propos…
Measurement-driven Security Analysis of Imperceptible Impersonation Attacks
Shasha Li, Karim Khalil, Rameswar Panda +4
The emergence of Internet of Things (IoT) brings about new security challenges at the intersection of cyber and physical spaces. One prime example is the vulnerability of Face Reco…
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
Shasha Li, Shitong Zhu, Sudipta Paul +5
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target ob…
GraphCL: Contrastive Self-Supervised Learning of Graph Representations
Hakim Hafidi, Mounir Ghogho, Philippe Ciblat +1
We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the…
Percolation Thresholds for Robust Network Connectivity
Arman Mohseni-Kabir, Mihir Pant, Don Towsley +2
Communication networks, power grids, and transportation networks are all examples of networks whose performance depends on reliable connectivity of their underlying network compone…
A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm
Huynh Thi Thanh Binh, Pham Dinh Thanh, Tran Ba Trung +4
Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables. LTGA performs w…
Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm
Kevin D. Smith, Saber Jafarpour, Ananthram Swami +1
Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology. However, the standard approach to topology mapping-…