title: Assortative mixing in close-packed spatial networks.
abstract: BACKGROUND
  In recent years, there is aroused interest in expressing complex systems as networks of interacting nodes. Using descriptors from graph theory, it has been possible to classify many diverse systems derived from social and physical sciences alike. In particular, folded proteins as examples of self-assembled complex molecules have also been investigated intensely using these tools. However, we need to develop additional measures to classify different systems, in order to dissect the underlying hierarchy.
  
  
  RESULTS
  In this study, a general analytical relation for the dependence of nearest neighbor degree correlations on degree is derived. Dependence of local clustering on degree is shown to be the sole determining factor of assortative versus disassortative mixing in networks. The characteristics of networks constructed from spatial atomic/molecular systems exemplified by self-organized residue networks built from folded protein structures and block copolymers, atomic clusters and well-compressed polymeric melts are studied. Distributions of statistical properties of the networks are presented. For these densely-packed systems, assortative mixing in the network construction is found to apply, and conditions are derived for a simple linear dependence.
  
  
  CONCLUSIONS
  Our analyses (i) reveal patterns that are common to close-packed clusters of atoms/molecules, (ii) identify the type of surface effects prominent in different close-packed systems, and (iii) associate fingerprints that may be used to classify networks with varying types of correlations.
authors.affiliation: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.
authors.forename: Deniz
authors.initials: D
authors.lastname: Turgut
authors.forename: Ali Rana
authors.initials: AR
authors.lastname: Atilgan
authors.forename: Canan
authors.initials: C
authors.lastname: Atilgan
mesh_terms: D000465:Algorithms; D002070:Butadienes / Q000737:chemistry; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D020724:Elastomers / Q000737:chemistry; D006801:Humans; D008823:Micelles; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D017510:Protein Folding; D012944:Social Support; D012997:Solvents; D049490:Systems Biology / Q000379:methods*
chemical_list: D002070:Butadienes; D020724:Elastomers; D008823:Micelles; D012997:Solvents; C028834:polybutadiene