Classes of complex networks defined by role-to-role connectivity profiles.

By Roger Guimerà, Marta Sales-Pardo, Luís A. Nunes Amaral. Nature Physics, 2007

431 citations recorded by the source corpus.

In physical, biological, technological and social systems, interactions between units give rise to intricate networks. These-typically non-trivial-structures, in turn, critically affect the dynamics and properties of the system. The focus of most current research on complex networks is, still, on global network properties. A caveat of this approach is that the relevance of global properties hinges on the premise that networks are homogeneous, whereas most real-world networks have a markedly modular structure. Here, we report that networks with different functions, including the Internet, metabolic, air transportation and protein interaction networks, have distinct patterns of connections among nodes with different roles, and that, as a consequence, complex networks can be classified into two distinct functional classes on the basis of their link type frequency. Importantly, we demonstrate that these structural features cannot be captured by means of often studied global properties.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/02fa24986cc4f4987441babd8f15d63077339170

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Citations in the corpus

Molecular ecological network analyses (2012, background): “In gene expression network analyses, the gene significance (GSi,h) is the correlation between the expression profile of the i-th gene and the h-th sample trait, Th [33].”
Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2 (2011, methodology): “The topological role of each OTU was determined according to the scatter plot of within-module connectivity (Zi) and among-module connectivity (Pi) (11, 34).”
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Link Prediction in Complex Networks: A Survey (2010, background): “The stochastic blockmodel can capture the community structure [22], role-to-role connections [94,95] and maybe other factors for the establishing of connections, especially when the group membership plays a considerable role in determining how nodes interact with each other, which usually could not ”
Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, background): “While the roles of nodes in a static network can be studied in multiple ways [28,29], we focus on describing the geometrical core-periphery organization— which can be used to help characterize the organization of edge strengths throughout a network —to compare it with the temporal core-periphery org”
Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans (2009, methodology): “First, we applied a network comparison algorithm [55] to compare the module-specific properties with the global properties of the brain networks.”
Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease (2012, methodology): “For functional modularity analysis, we used Newman’s modularity metric combined with a simulated annealing process (previously described in [55,86]).”
Structure and dynamics of core/periphery networks (2013, background): “[118] assessing the topology of hubs and non-hub nodes in modular networks.”
Precision functional mapping of individual human brains (2017, neutral): “Therefore, we also examined the reliability of three measures commonly employed to evaluate these properties: 3) the participation coefficient (PC), a measure of the diversity of a region’s cross-network connectivity contributing to its hub status (Guimerà et al., 2007; Power et al., 2013); 4) globa”
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