Covariance and correlation estimators in bipartite complex systems with a double heterogeneity

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Abstract

Complex bipartite systems are studied in Biology, Physics, Economics, and Social Sciences, and they can suitably be described as bipartite networks. The heterogeneity of elements in those systems makes it very difficult to perform a statistical analysis of similarity starting from empirical data. Though binary Pearson's correlation coefficient has proved effective to investigate the similarity structure of some real-world bipartite networks, here we show that both the usual sample covariance and correlation coefficient are affected by a bias, which is due to the aforementioned heterogeneity. Such a bias affects real bipartite systems, and, for example, we report its effects on empirical data from two bipartite systems. Therefore, we introduce weighted estimators of covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators is that they are unbiased and, therefore, better suited to investigate the similarity structure of bipartite systems with a double layer of heterogeneity. We apply the introduced estimators to two bipartite systems, one social and the other biological. Such an analysis shows that weighted estimators better reveal emergent properties of these systems than unweighted ones.
Lingua originaleEnglish
pagine (da-a)053404-
Numero di pagine28
RivistaJournal of Statistical Mechanics: Theory and Experiment
Volume2019
Stato di pubblicazionePublished - 2019

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complex systems
estimators
Complex Systems
Estimator
correlation coefficients
Bipartite Network
Correlation coefficient
biology
statistical analysis
economics
Pearson Correlation
Social Sciences
physics
Complex systems
coefficients
Biology
Statistical Analysis
Physics
Economics
Binary

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cita questo

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title = "Covariance and correlation estimators in bipartite complex systems with a double heterogeneity",
abstract = "Complex bipartite systems are studied in Biology, Physics, Economics, and Social Sciences, and they can suitably be described as bipartite networks. The heterogeneity of elements in those systems makes it very difficult to perform a statistical analysis of similarity starting from empirical data. Though binary Pearson's correlation coefficient has proved effective to investigate the similarity structure of some real-world bipartite networks, here we show that both the usual sample covariance and correlation coefficient are affected by a bias, which is due to the aforementioned heterogeneity. Such a bias affects real bipartite systems, and, for example, we report its effects on empirical data from two bipartite systems. Therefore, we introduce weighted estimators of covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators is that they are unbiased and, therefore, better suited to investigate the similarity structure of bipartite systems with a double layer of heterogeneity. We apply the introduced estimators to two bipartite systems, one social and the other biological. Such an analysis shows that weighted estimators better reveal emergent properties of these systems than unweighted ones.",
author = "Michele Tumminello and Pietro Vassallo and Elena Puccio and Jyrki Piilo",
year = "2019",
language = "English",
volume = "2019",
pages = "053404--",
journal = "Journal of Statistical Mechanics: Theory and Experiment",
issn = "1742-5468",
publisher = "IOP Publishing Ltd.",

}

TY - JOUR

T1 - Covariance and correlation estimators in bipartite complex systems with a double heterogeneity

AU - Tumminello, Michele

AU - Vassallo, Pietro

AU - Puccio, Elena

AU - Piilo, Jyrki

PY - 2019

Y1 - 2019

N2 - Complex bipartite systems are studied in Biology, Physics, Economics, and Social Sciences, and they can suitably be described as bipartite networks. The heterogeneity of elements in those systems makes it very difficult to perform a statistical analysis of similarity starting from empirical data. Though binary Pearson's correlation coefficient has proved effective to investigate the similarity structure of some real-world bipartite networks, here we show that both the usual sample covariance and correlation coefficient are affected by a bias, which is due to the aforementioned heterogeneity. Such a bias affects real bipartite systems, and, for example, we report its effects on empirical data from two bipartite systems. Therefore, we introduce weighted estimators of covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators is that they are unbiased and, therefore, better suited to investigate the similarity structure of bipartite systems with a double layer of heterogeneity. We apply the introduced estimators to two bipartite systems, one social and the other biological. Such an analysis shows that weighted estimators better reveal emergent properties of these systems than unweighted ones.

AB - Complex bipartite systems are studied in Biology, Physics, Economics, and Social Sciences, and they can suitably be described as bipartite networks. The heterogeneity of elements in those systems makes it very difficult to perform a statistical analysis of similarity starting from empirical data. Though binary Pearson's correlation coefficient has proved effective to investigate the similarity structure of some real-world bipartite networks, here we show that both the usual sample covariance and correlation coefficient are affected by a bias, which is due to the aforementioned heterogeneity. Such a bias affects real bipartite systems, and, for example, we report its effects on empirical data from two bipartite systems. Therefore, we introduce weighted estimators of covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators is that they are unbiased and, therefore, better suited to investigate the similarity structure of bipartite systems with a double layer of heterogeneity. We apply the introduced estimators to two bipartite systems, one social and the other biological. Such an analysis shows that weighted estimators better reveal emergent properties of these systems than unweighted ones.

UR - http://hdl.handle.net/10447/366092

M3 - Article

VL - 2019

SP - 053404-

JO - Journal of Statistical Mechanics: Theory and Experiment

JF - Journal of Statistical Mechanics: Theory and Experiment

SN - 1742-5468

ER -