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Open AccessJournal ArticleDOI

Degree and clustering coefficient in sparse random intersection graphs

TLDR
Asymptotic vertex degree distribution is established and its relation to the clustering coecient in two popular random intersection graph models of Godehardt and Jaworski (2001) and for sparse graphs with positive clusteringCoecient is examined.
Abstract
We establish asymptotic vertex degree distribution and examine its relation to the clustering coefficient in two popular random intersection graph models of Godehardt and Jaworski [Electron. Notes Discrete Math. 10 (2001) 129-132]. For sparse graphs with a positive clustering coefficient, we examine statistical dependence between the (local) clustering coefficient and the degree. Our results are mathematically rigorous. They are consistent with the empirical observation of Foudalis et al. [In Algorithms and Models for Web Graph (2011) Springer] that, "clustering correlates negatively with degree." Moreover, they explain empirical results on $k^{-1}$ scaling of the local clustering coefficient of a vertex of degree k reported in Ravasz and Barabasi [Phys. Rev. E 67 (2003) 026112].

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Citations
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Journal ArticleDOI

Wireless Sensor Network Based Smart Grid Communications: Cyber Attacks, Intrusion Detection System and Topology Control

TL;DR: This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.
Proceedings ArticleDOI

On the strengths of connectivity and robustness in general random intersection graphs

TL;DR: In this article, the authors investigated the strength of connectivity and robustness in a general random intersection graph model and established sharp asymptotic zero-one laws for $k-connectivity and $k$-robustness.
Book ChapterDOI

Recent Progress in Complex Network Analysis: Properties of Random Intersection Graphs

TL;DR: This work surveys recent results concerning various random intersection graph models showing that they have tunable clustering coefficient, a rich class of degree distributions including power-laws, and short average distances.
Journal ArticleDOI

On Resilience and Connectivity of Secure Wireless Sensor Networks Under Node Capture Attacks

TL;DR: In this paper, the resilience of the $q$ -composite key predistribution scheme to node capture attacks is analyzed. But, the resilience is not defined precisely and under realistic conditions.
Journal ArticleDOI

Clustering and the Hyperbolic Geometry of Complex Networks

TL;DR: This article considers the global clustering coefficient of random graphs on the hyperbolic plane, proposed recently by Krioukov and colleagues as a mathematical model of complex networks, under the fundamental assumption thathyperbolic geometry underlies the structure of these networks.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Proceedings ArticleDOI

A key-management scheme for distributed sensor networks

TL;DR: A key-management scheme designed to satisfy both operational and security requirements of DSNs is presented, which relies on probabilistic key sharing among the nodes of a random graph and uses simple protocols for shared-key discovery and path-key establishment, and for key revocation, re-keying, and incremental addition of nodes.
Journal ArticleDOI

Random graphs with arbitrary degree distributions and their applications.

TL;DR: It is demonstrated that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world, while in others there is a measurable discrepancy between theory and reality, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.
Journal ArticleDOI

Hierarchical organization in complex networks

TL;DR: In this article, the authors show that many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale free topology.
Journal ArticleDOI

Random graph models of social networks

TL;DR: It is found that in some cases, the models are in remarkable agreement with the data, whereas in others the agreement is poorer, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.