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 …
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 …
--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 …
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 …
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 …
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, …
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 …
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 …
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 …
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 …
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 …
