Article Page

Abstract

Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems. The search for information in the World Wide Web (WWW) through search engines has turned into a daily habit and an essential tool to fulfill most of our work duties. An ideal search engine looks for the information the user is querying amongst billions of webpages in real time, and produces a ranking of the results sorted according the user expectations. Although not being among the first search engines available, the Google search engine was the first to achieve these goals efficiently, establishing one of the milestones of the digital era. Its main novelty was to classify and rank webpages based on the interrelations created between them through the hyperlinks1, rather than using only their intrinsic features (such as the page content). Google’s ranking algorithm, known as Pagerank2 (PR), is rooted in a diffusion process that mimics the user’s navigation through webpages as the motion of a random walker following a shannon entropy Quantum Navigation and Ranking hyperlink pathways. in Complex Networks descriptors (SHED) for the in silico prediction of an annotated suitable lead chemo-recored compound as a potent computer predicted inhibitor comprising potential hyper-mimicking activities to 5 conserved anti-plasmodium peptides.

Keywords

Quantum Navigation; Ranking in Complex Networks; shannon entropy descriptor; (SHED) for the in silico prediction; annotated suitable; lead chemo-recored compound; potent computer predicted inhibitor; hyper-mimicking activities; 5 conserved; anti-plasmodium peptides.

Article Type

Research Article – Abstract

Publication history

Received: Sep 20, 2017
Accepted: Sep 25, 2017
Published: Oct 01, 2017

Citation

Grigoriadis Ioannis, Grigoriadis George, Grigoriadis Nikolaos, George Galazios (2017) A shannon entropy Quantum Navigation and Ranking in Complex Networks descriptors (SHED) for the in silico prediction of an annotated suitable lead chemo-recored compound as a potent computer predicted inhibitor comprising potential hyper-mimicking activities to 5 conserved anti-plasmodium peptides.

Authors Info

Grigoriadis Nikolaos
Department of IT Computer Aided Personalized Myoncotherapy, Cartigenea-Cardiogenea, Neurogenea-Cellgenea, Cordigenea-HyperoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis Ioannis
Department of Computer Drug Discovery Science, BiogenetoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis George
Department of Stem Cell Bank and ViroGeneaTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

George Galazios
Professor of Obstetrics and Gynecology,
Democritus University of Thrace,
Komotini, Greece;

E-mail: biogeneadrug@gmail.com

File not available