scholarly journals New Inequalities between Information Measures of Network Information Content

2013 ◽  
Vol 2013 ◽  
pp. 1-3 ◽  
Author(s):  
Pantelimon-George Popescu ◽  
Florin Pop ◽  
Alexandru Herişanu ◽  
Nicolae Ţăpuş
Keyword(s):  
Information Processing ◽  
Complex Systems ◽  
Complex Networks ◽  
Information Content ◽  
Discrete Case ◽  
Network Information ◽  
Information Inequalities ◽  
Information Measures ◽  
New Inequalities

We refine a classical logarithmic inequality using a discrete case of Bernoulli inequality, and then we refine furthermore two information inequalities between information measures for graphs, based on information functionals, presented by Dehmer and Mowshowitz in (2010) as Theorems 4.7 and 4.8. The inequalities refer to entropy-based measures of network information content and have a great impact for information processing in complex networks (a subarea of research in modeling of complex systems).

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

scholarly journals Detecting Determinism in Time Series with Complex Networks Constructed Using a Compression Algorithm

2018 ◽  
Vol 28 (13) ◽  
pp. 1850165
Author(s):  
Débora C. Corrêa ◽  
David M. Walker ◽  
Michael Small
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Information Content ◽  
Compression Algorithm ◽  
Connectivity Structure ◽  
Cycle Basis ◽  
Minimal Cycle ◽  
Time Irreversibility ◽  
Basis Structure

The properties of complex networks derived from applying a compression algorithm to time series subject to symbolic ordinal-based encoding is explored. The information content of compression codewords can be used to detect forbidden symbolic patterns indicative of nonlinear determinism. The connectivity structure of ordinal-based compression networks summarized by their minimal cycle basis structure can also be used in tests for nonlinear determinism, in particular, detection of time irreversibility in a signal.

scholarly journals The interconnection exchange and complex systems properties in power grid network

2021 ◽  
Vol 336 ◽  
pp. 05020
Author(s):  
Piotr Hadaj ◽  
Marek Nowak ◽  
Dominik Strzałka
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Real Data ◽  
Electrical Energy ◽  
Significant Drop ◽  
Average Efficiency ◽  
Grid Network ◽  
System Operator ◽  
Energy Network

A case study based on the real data obtained from the Polish PSE System Operator of the highest voltages electrical energy network is shown. The data about the interconnection exchange and some complex networks (graphs) parameters were examined, after the removal of selected nodes. This allowed to test selected network parameters and to show that the breakdown of only three nodes in this network can cause significant drop of its average efficiency.

An Introduction to Complex Networks

2013 ◽  
Author(s):  
Jordi Bascompte ◽  
Pedro Jordano
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Network Structure ◽  
Ecological Networks ◽  
Broad Context ◽  
Network Approach ◽  
Mutualistic Networks ◽  
Plant Animal Interactions

Mutualisms can involve dozens, even hundreds, of species and this complexity has precluded a serious community-wide approach to plant–animal interactions. The most straightforward way to describe such an interacting community is with a network of interactions. In this approach, species are represented as nodes of two types: plants and animals. This chapter provides the tools and concepts for characterizing mutualistic networks and placing them into a broad context. This serves as a background with which to understand the structure of mutualistic networks. The discussions cover a network approach to complex systems, measures of network structure, models of network buildup, and ecological networks.

Design and Implementation of Chinese Information Filtering System

2013 ◽  
Vol 756-759 ◽  
pp. 1164-1167
Author(s):  
Xiu Mei Zhang
Keyword(s):  
Information Processing ◽  
Vector Space ◽  
Information Filtering ◽  
Experimental Results ◽  
Important Research ◽  
Index Method ◽  
Network Information ◽  
Design And Implementation ◽  
Tree Index ◽  
Space Method

With the development of the network information, information processing is vital in all aspects. Information filtering is more important research aspect. Especially Chinese information filtering is urgent affairs. According to research of the domestic and abroad, in the article vector space method and hyB+ tree index method is combined to filter text. Experimental results show that, this method is feasible.

scholarly journals The sleeping brain as a complex system

2011 ◽  
Vol 369 (1952) ◽  
pp. 3697-3707 ◽  
Author(s):  
Eckehard Olbrich ◽  
Peter Achermann ◽  
Thomas Wennekers
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Systems Approach ◽  
Complex Dynamics ◽  
Activity Patterns ◽  
Complexity Science ◽  
Sleep Stages ◽  
Sleep Regulation ◽  
Theme Issue ◽  
The Brain

‘Complexity science’ is a rapidly developing research direction with applications in a multitude of fields that study complex systems consisting of a number of nonlinear elements with interesting dynamics and mutual interactions. This Theme Issue ‘The complexity of sleep’ aims at fostering the application of complexity science to sleep research, because the brain in its different sleep stages adopts different global states that express distinct activity patterns in large and complex networks of neural circuits. This introduction discusses the contributions collected in the present Theme Issue. We highlight the potential and challenges of a complex systems approach to develop an understanding of the brain in general and the sleeping brain in particular. Basically, we focus on two topics: the complex networks approach to understand the changes in the functional connectivity of the brain during sleep, and the complex dynamics of sleep, including sleep regulation. We hope that this Theme Issue will stimulate and intensify the interdisciplinary communication to advance our understanding of the complex dynamics of the brain that underlies sleep and consciousness.

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