English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Finding strongly connected components (SCCs) and the diameter of a directednetwork play a key role in a variety of discrete optimization problems, andsubsequently, machine learning and control theory problems. On the one hand,SCCs are used in solving the 2-satisfiability problem, which has applicationsin clustering, scheduling, and visualization. On the other hand, the diameterhas applications in network learning and discovery problems enabling efficientinternet routing and searches, as well as identifying faults in the power grid.  In this paper, we leverage consensus-based principles to find the SCCs in ascalable and distributed fashion with a computational complexity of$\mathcal{O}\left(Dd_{\text{in-degree}}^{\max}\right)$, where $D$ is the(finite) diameter of the network and $d_{\text{in-degree}}^{\max}$ is themaximum in-degree of the network. Additionally, we prove that our algorithmterminates in $D+1$ iterations, which allows us to retrieve the diameter of thenetwork. We illustrate the performance of our algorithm on several randomnetworks, including Erd\H{o}s-R\'enyi, Barab\'asi-Albert, and\mbox{Watts-Strogatz} networks.

A scalable distributed dynamical systems approach to compute the strongly connected components and diameter of networks