Network Science
Event-Triggered Control Design for Systems With Exogenous Inputs: Application for Auto-Scaling of Cloud-Hosted Web Servers
In this article, an event-triggered control (ETC) law is proposed for auto-scaling of Web servers hosted on a private cloud. The Web server systems are modeled as a discrete-time linear time-invariant (LTI) system where …
Effect of dormant spare capacity on the attack tolerance of complex networks
Benchmarking and Analysing Unsupervised Network Representation Learning and the Illusion of Progress
A number of methods have been developed for unsupervised network representation learning – ranging from classical methods based on the graph spectra to recent random walk based methods and from deep learning based …
Controllability of Functional Brain Networks
--Prof. Ramkrishna Pasumarthy-- The brain has undeniably been the most enigmatic organ of the human body. To further our understanding of this mysterious organ, a talk titled “Controllability of Functional Brain …
Optimizing Driver Nodes for Structural Controllability of Temporal Networks
In this article, we derive conditions for structural controllability of temporal networks that change topology and edge weights with time. The existing results for structural controllability of directed networks assume …
Discovering adaptation-capable biological network structures using control-theoretic approaches
Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every …
Verification and Rectification of Error in Topology of Conserved Networks
The knowledge of the underlying topology is essential for understanding and manipulating power grids, water distribution networks, biological networks. At times, the topology may be reported (or recorded) erroneously, …
On Strong Structural Controllability of Temporal Networks
In this letter, we study strong structural controllability of linear time varying network systems that change network topology and edge weights with time. We derive graph based necessary and sufficient conditions for …
Metric Learning for comparison of HMMs using Graph Neural Networks
Hidden Markov models (HMMs) belong to the class of double embedded stochastic models which were originally leveraged for speech recognition and synthesis. HMMs subsequently became a generic sequence model across multiple …
Predicting unknown directed links of conserved networks from flow data
Abstract Link prediction between nodes is an important problem in the study of complex networks. In this work, we investigate determining directed links in conserved flow networks from data. A novel approach to predict …
A Joint Training Framework for Open-World Knowledge Graph Embeddings
Knowledge Graphs(KGs) represent factual information as graphs of entities connected by relations. Knowledge graph embeddings have emerged as a popular approach to encode this information for various downstream tasks like …
Learning Mesh and Multiple Conserved Networks From Data
Reconstruction of network topology from data is one of the important problem in network science. Earlier, it has been shown that conserved tree-type (or radial) networks can be reconstructed from flow data exactly by …