Randomness Index for complex network analysis View Full Text


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Article Info

DATE

2017-06-12

AUTHORS

Natarajan Meghanathan

ABSTRACT

The high-level contribution of this paper is a quantitative measure (called Randomness Index) to assess the extent of randomness in the topology of a complex real-world network. We exploit the observation that the local clustering coefficient (LCC) for a node in a truly random network is independent of the degree of the node and is simply the probability for a link to exist between any two nodes in the network. On the other hand, for real-world networks that are not truly random, nodes with a larger degree are more likely to have a lower LCC value and vice versa. For any complex real-world network, we propose to determine the Randomness Index as the Pearson’s correlation coefficient (ranging from −1 to 1) of the degree versus average LCC of the nodes with the particular degree. We evaluate the Randomness Index values for a suite of 48 real-world networks of diverse degree distribution and observe the median value to be −0.72. More... »

PAGES

25

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13278-017-0444-3

DOI

http://dx.doi.org/10.1007/s13278-017-0444-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085982883


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