Result: Transtemporal edges and crosslayer edges in incompressible high-order networks

Title:
Transtemporal edges and crosslayer edges in incompressible high-order networks
Authors:
Klaus Wehmuth, Artur Ziviani, Felipe S. Abrahão
Source:
Anais do Encontro de Teoria da Computação (ETC).
Publication Status:
Preprint
Publisher Information:
Sociedade Brasileira de Computação - SBC, 2019.
Publication Year:
2019
Subject Terms:
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Logic in Computer Science, time-varying graphs, Discrete Mathematics (cs.DM), algorithmic complexity, Computer Science - Information Theory, Information Theory (cs.IT), General Topology (math.GN), Computer Science - Social and Information Networks, 0102 computer and information sciences, 01 natural sciences, Logic in Computer Science (cs.LO), algorithmic randomness, 0103 physical sciences, FOS: Mathematics, generalized graphs, multiaspect graphs, Computer Science - Discrete Mathematics, Mathematics - General Topology
Document Type:
Academic journal Article<br />Conference object<br />Other literature type
DOI:
10.5753/etc.2019.6389
DOI:
10.48550/arxiv.1905.05276
DOI:
10.5281/zenodo.2798124
DOI:
10.5281/zenodo.2798123
Access URL:
http://arxiv.org/pdf/1905.05276
http://arxiv.org/abs/1905.05276
https://dblp.uni-trier.de/db/journals/corr/corr1905.html#abs-1905-05276
http://ui.adsabs.harvard.edu/abs/2019arXiv190505276A/abstract
Rights:
arXiv Non-Exclusive Distribution
CC BY
Accession Number:
edsair.doi.dedup.....c58b906b94cb7dea68ef5fd6cc50dcfc
Database:
OpenAIRE

Further Information

This work presents some outcomes of a theoretical investigation of incompressible high-order networks defined by a generalized graph represen tation. We study some of their network topological properties and how these may be related to real world complex networks. We show that these networks have very short diameter, high k-connectivity, degrees of the order of half of the network size within a strong-asymptotically dominated standard deviation, and rigidity with respect to automorphisms. In addition, we demonstrate that incompressible dynamic (or dynamic multilayered) networks have transtemporal (or crosslayer) edges and, thus, a snapshot-like representation of dynamic networks is inaccurate for capturing the presence of such edges that compose underlying structures of some real-world networks.