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Title: Cross-linked structure of network evolution

We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.
Authors:
 [1] ;  [2] ;  [2] ; ;  [3] ;  [4] ;  [5] ;  [6] ;  [2]
  1. Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)
  2. (United States)
  3. Department of Psychology and UCSB Brain Imaging Center, University of California, Santa Barbara, California 93106 (United States)
  4. Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom)
  5. (United Kingdom)
  6. Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599 (United States)
Publication Date:
OSTI Identifier:
22251599
Resource Type:
Journal Article
Resource Relation:
Journal Name: Chaos (Woodbury, N. Y.); Journal Volume: 24; Journal Issue: 1; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; BRAIN; EVOLUTION; LEARNING; MAPS; NONLINEAR PROBLEMS; OSCILLATORS; VARIATIONS