Are all the word ranking methods the same?

2021 ◽  
pp. 2150144
Author(s):  
Elham Najafi ◽  
Alireza Valizadeh ◽  
Amir H. Darooneh
Keyword(s):  
Complex System ◽  
Time Series ◽  
Spatial Distribution ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Rank Correlation ◽  
Ranking Methods ◽  
Series Analysis ◽  
Maximum Value

Text as a complex system is commonly studied by various methods, like complex networks or time series analysis, in order to discover its properties. One of the most important properties of each text is its keywords, which are extracted by word ranking methods. There are various methods to rank words of a text. Each method differently ranks words according to their frequency, spatial distribution or other word properties. Here, we aimed to explore how similar various word ranking methods are. For this purpose, we studied the rank correlation of some important word ranking methods for number of sample texts with different subjects and text sizes. We found that by increasing text size the correlation between ranking methods grows. It means that as the text size increases, the associated word ranks calculated by different ranking methods converge. Also, we found out that the rank correlations of word ranking methods approach their maximum value in the case of large enough texts.

scholarly journals Nonlinear time series analysis by means of complex networks

2019 ◽  
Vol 50 (1) ◽  
pp. 010509
Author(s):  
null DONNER Reik V. ◽  
MARWAN Norbert ◽  
ZOU Yong ◽  
KURTHS Jürgen ◽  
null DONGES Jonathan F.
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis

scholarly journals Novel measures based on the Kolmogorov complexity for use in complex system behavior studies and time series analysis

Open Physics ◽  
2015 ◽  
Vol 13 (1) ◽  
Author(s):  
Dragutin T. Mihailović ◽  
Gordan Mimić ◽  
Emilija Nikolić-Djorić ◽  
Ilija Arsenić
Keyword(s):  
Complex System ◽  
Time Series ◽  
Time Series Analysis ◽  
Kolmogorov Complexity ◽  
River Flow ◽  
Cell System ◽  
System Behavior ◽  
Series Analysis ◽  
Biochemical Substance

AbstractWe propose novel metrics based on the Kolmogorov complexity for use in complex system behavior studies and time series analysis. We consider the origins of the Kolmogorov complexity and discuss its physical meaning. To get better insights into the nature of complex systems and time series analysis we introduce three novel measures based on the Kolmogorov complexity: (i) the Kolmogorov complexity spectrum, (ii) the Kolmogorov complexity spectrum highest value and (iii) the overall Kolmogorov complexity. The characteristics of these measures have been tested using a generalized logistic equation. Finally, the proposed measures have been applied to different time series originating from: a model output (the biochemical substance exchange in a multi-cell system), four different geophysical phenomena (dynamics of: river flow, long term precipitation, indoor

Time series analysis in earthquake complex networks

2018 ◽  
Vol 28 (8) ◽  
pp. 083128 ◽  
Author(s):  
Denisse Pastén ◽  
Zbigniew Czechowski ◽  
Benjamín Toledo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Series Analysis

NONLINEAR TIME SERIES INTO COMPLEX NETWORKS SCHEME

2010 ◽  
Vol 20 (02) ◽  
pp. 413-417 ◽  
Author(s):  
FRANCISCO O. REDELICO ◽  
ARACELI N. PROTO
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Simulated Data ◽  
Nonlinear Time Series ◽  
New Method ◽  
Series Analysis ◽  
Extract Information

Time series analysis is a fundamental tool for a wide variety of fields. Different methods have been proposed to extract information about the underlying dynamics (basically nonlinear) contained inside the time series [Albert & Barabási, 2002]. In this contribution, a new method for mapping nonlinear time series into complex networks is studied using simulated data.

scholarly journals Brewing unit time series analysis in the research of the complex system attractor properties

2014 ◽  
Vol 6 (2(72)) ◽  
pp. 38
Author(s):  
Микола Володимирович Чернецький ◽  
Василь Дмитрович Кишенько
Keyword(s):  
Complex System ◽  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
System Attractor

scholarly journals RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS

2011 ◽  
Vol 21 (04) ◽  
pp. 1019-1046 ◽  
Author(s):  
REIK V. DONNER ◽  
MICHAEL SMALL ◽  
JONATHAN F. DONGES ◽  
NORBERT MARWAN ◽  
YONG ZOU ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Complex Network ◽  
Structural Features ◽  
Recurrence Plots ◽  
Series Analysis ◽  
Network Methods ◽  
The Individual

Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. During the last years, intensive efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant higher-order statistical properties of time series. Notably, many corresponding approaches are closely related to the concept of recurrence in phase space. In this paper, we review recent methodological advances in time series analysis based on complex networks, with a special emphasis on methods founded on recurrence plots. The potentials and limitations of the individual methods are discussed and illustrated for paradigmatic examples of dynamical systems as well as for real-world time series. Complex network measures are shown to provide information about structural features of dynamical systems that are complementary to those characterized by other methods of time series analysis and, hence, substantially enrich the knowledge gathered from other existing (linear as well as nonlinear) approaches.

Sign in / Sign up

Export Citation Format

Share Document