Spectral centrality measures in complex networks.

@article{Perra2008SpectralCM,
  title={Spectral centrality measures in complex networks.},
  author={Nicola Perra and Santo Fortunato},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2008},
  volume={78 3 Pt 2},
  pages={
          036107
        }
}
  • N. Perra, S. Fortunato
  • Published 2008
  • Mathematics, Physics, Medicine
  • Physical review. E, Statistical, nonlinear, and soft matter physics
Complex networks are characterized by heterogeneous distributions of the degree of nodes, which produce a large diversification of the roles of the nodes within the network. Several centrality measures have been introduced to rank nodes based on their topological importance within a graph. Here we review and compare centrality measures based on spectral properties of graph matrices. We shall focus on PageRank (PR), eigenvector centrality (EV), and the hub and authority scores of the HITS… Expand
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References

SHOWING 1-10 OF 41 REFERENCES
Subgraph centrality in complex networks.
TLDR
A new centrality measure that characterizes the participation of each node in all subgraphs in a network, C(S)(i), which is better able to discriminate the nodes of a network than alternate measures such as degree, closeness, betweenness, and eigenvector centralities. Expand
Statistical-mechanical approach to subgraph centrality in complex networks
TLDR
The subgraph centrality is interpreted as the partition function of a network and several models of network growing/evolution as well as real-world networks, such as those representing metabolic and protein–protein interaction networks aswell as the interaction between secondary structure elements in proteins are explored. Expand
Spectral measures of bipartivity in complex networks.
TLDR
It is shown that the bipartivity characterizes the network structure and can be related to the efficiency of semantic or communication networks, trophic interactions in food webs, construction principles in metabolic networks, or communities in social networks. Expand
A set of measures of centrality based upon betweenness
TLDR
A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced, used to index centrality in any large or small network of symmetrical relations, whether connected or unconnected. Expand
Eigenvector-like measures of centrality for asymmetric relations
TLDR
An alternative measure of centrality is suggested that equals an eigenvector when eigenvectors can be used and provides meaningfully comparable results when they cannot. Expand
The Structure and Function of Complex Networks
  • M. Newman
  • Physics, Computer Science
  • SIAM Rev.
  • 2003
TLDR
Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks. Expand
Random Walks on Directed Networks: the Case of PageRank
PageRank, the prestige measure for Web pages used by Google, is the stationary probability of a peculiar random walk on directed graphs, which interpolates between a pure random walk and a processExpand
Epidemic spreading in scale-free networks.
TLDR
A dynamical model for the spreading of infections on scale-free networks is defined, finding the absence of an epidemic threshold and its associated critical behavior and this new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks. Expand
Emergence of scaling in random networks
TLDR
A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems. Expand
Complex networks: Structure and dynamics
Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highlyExpand
...
1
2
3
4
5
...