Degree and clustering coefficient in sparse random intersection graphs
By Mindaugas Bloznelis. 2013
109 citations recorded by the source corpus.
We establish asymptotic vertex degree distribution and examine its relation to the clustering coecient in two popular random intersection graph models of Godehardt and Jaworski (2001). For sparse graphs with positive clustering coecient, we examine statistical dependence between the (local) clustering coecient and the degree. Our results are mathematically rigorous. They are consistent with the empirical observation of Foudalis et al. (2011) that \clustering correlates negatively with degree." Moreover, they explain empirical results on k 1 scaling of the local clustering coecient of a vertex of degree k reported in Ravasz and Barab asi (2003).
Subfields: Mathematical Physics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5c4e4694b330bd4176c96db211e3cbcbabe6d280
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In Mathematical Physics
In Statistical and Nonlinear Physics
In Statistics and Probability
Citations in the corpus
- Advances in Social Networks Analysis and Mining ( ASONAM ) DE-Crawler : A Densification-Expansion Algorithm for Online Data Collection (2018, background): “This formula is motivated by the observation that hub nodes tend to have high degree and low clustering coefficient [14].”