Search Results for author:
Found 16 papers, 4 papers with code
A Geometric Approach to Resilient Distributed Consensus Accounting for State Imprecision and Adversarial Agents
The safe point of an agent lies within the convex hull of its \emph{normal} neighbors' states and hence is used by the agent to update it's state.
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs.
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks.
Controllability Backbone in Networks
Thus, we utilize two lower bounds on the network's SSC based on the zero forcing notion and graph distances.
A Survey of Graph Unlearning
By laying a solid foundation and fostering continued progress, this survey seeks to inspire researchers to further advance the field of graph unlearning, thereby instilling confidence in the ethical growth of AI systems and reinforcing the responsible application of machine learning techniques in various domains.
Distributed Design of Controllable and Robust Networks using Zero Forcing and Graph Grammars
This paper studies the problem of designing networks that are strong structurally controllable, and robust simultaneously.
Resilient Strong Structural Controllability in Networks using Leaky Forcing in Graphs
We consider three types of misbehaving nodes and edges that disrupt the zero forcing process in graphs, thus, deteriorating the network SSC.
Computing Graph Descriptors on Edge Streams
Operating on edge streams allows us to avoid storing the entire graph in memory, and controlling the sample size enables us to keep the runtime of our algorithms within desired bounds.
Edge Augmentation with Controllability Constraints in Directed Laplacian Networks
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 controllability (SSC).
Byzantine Resilient Distributed Multi-Task Learning
We analyze the approach for convex models and show that normal agents converge resiliently towards the global minimum. Further, aggregation with the proposed weight assignment rule always results in an improved expected regret than the non-cooperative case.
Resilient Distributed Vector Consensus Using Centerpoints
In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms.
Estimating Descriptors for Large Graphs
State-of-the-art algorithms for computing descriptors require the entire graph to be in memory, entailing a huge memory footprint, and thus do not scale well to increasing sizes of real-world networks.
Databases
Patch-based Generative Adversarial Network Towards Retinal Vessel Segmentation
Retinal blood vessels are considered to be the reliable diagnostic biomarkers of ophthalmologic and diabetic retinopathy.
Computation of the Distance-based Bound on Strong Structural Controllability in Networks
The bound is based on a sequence of vectors containing the distances between leaders (nodes with external inputs) and followers (remaining nodes) in the underlying network graph.
Locomotion and gesture tracking in mice and small animals for neurosceince applications: A survey
Neuroscience has traditionally relied on manually observing lab animals in controlled environments.
Soft Computing Techniques for Dependable Cyber-Physical Systems
Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements.
