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Related Concept Videos

Relationship Formation02:12

Relationship Formation

What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
Complexation Equilibria: Factors Influencing Stability of Complexes01:09

Complexation Equilibria: Factors Influencing Stability of Complexes

In complexation reactions, metal cations are the electron pair acceptors, and the ligands are the electron pair donors. The stability of the metal complexes depends primarily on the complexing ability of the central metal ion and the nature of the ligands. Generally, the complexing ability of the metal ion depends on the size and charge of the ion. As the metal ion size increases, the stability of the metal complexes decreases, provided that the valency of the metal ion and the ligands remain...
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

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Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...

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Correlations between community structure and link formation in complex networks.

Zhen Liu1, Jia-Lin He, Komal Kapoor

  • 1Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, ChengDu, SiChuan, China ; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America.

Plos One
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

Complex network links tend to form communities, revealing underlying formation mechanisms. A new computational framework accurately predicts missing links by understanding how these communities preferentially form cliques.

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Area of Science:

  • Network science
  • Data mining
  • Computational biology
  • Social network analysis

Background:

  • Complex networks feature links representing specific ties between nodes.
  • Understanding link formation mechanisms in these networks is a significant challenge.

Purpose of the Study:

  • To investigate the correlation between community structure and link formation.
  • To develop a computational framework for analyzing link formation in complex networks.

Main Methods:

  • Developed a general computational framework for network partitioning and link probability estimation.
  • Utilized the framework to identify missing links in partially observed networks.

Main Results:

  • Identified that links preferentially form cliques within communities, enhancing local clustering.
  • Demonstrated the framework's efficiency in accurately predicting missing links.

Conclusions:

  • Provided new insights into link creation mechanisms within communities.
  • The computational framework supports applications like community detection and missing link prediction.