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Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management
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Ordered visibility graph average aggregation operator: An application in produced water management.

Wen Jiang1, Boya Wei1, Yongchuan Tang1

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.

Chaos (Woodbury, N.Y.)
|March 3, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel ordered visibility graph average aggregation operator for complex systems. The method enhances data aggregation by considering value order and inter-value correlations, improving upon existing techniques.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Data Aggregation

Background:

  • Complex networks are essential for modeling intricate systems.
  • Effective data aggregation remains a significant challenge in complex systems analysis.
  • Existing aggregation methods often overlook the interplay between data order and relationships.

Purpose of the Study:

  • To propose a new data aggregation operator for complex systems.
  • To develop a method that incorporates both data ordering and inter-value correlations.
  • To demonstrate the efficacy of the proposed operator in practical applications.

Main Methods:

  • An ordered visibility graph average aggregation operator was developed.
  • Data values were sorted in descending order.
  • A novel support function measured relationships within the visibility graph.
  • A weighted network determined the significance of each data value.

Main Results:

  • The proposed operator effectively aggregates data by considering value order and correlation.
  • It offers a more comprehensive approach compared to traditional aggregation methods.
  • The operator demonstrated efficiency in a case study of produced water management.

Conclusions:

  • The ordered visibility graph average aggregation operator provides a universal solution for data aggregation in complex systems.
  • This method enhances the accuracy and robustness of data analysis.
  • The approach has broad applicability across various complex system domains.