Weighted assortative and disassortative networks model

Leung, Cyril; Chau, H. F.

doi:10.1016/j.physa.2006.12.022

Cited by 107 publications

(79 citation statements)

References 29 publications

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“…For topological analysis of the UW ud London metro network, we examine whether the network exhibits a small-world effect. Watts and Strogatz introduced the concept of small-world effect in networks, characterized by a small characteristic path length (CPL)-the average shortest path length for the network, and a high clustering coefficient (CC) [50]-a measure of clustering in the network [51]. For this reason, nodes in a small-world network are not connected directly to each other but most nodes can be reached indirectly from all other nodes by a small number of steps [52].…”

Section: Topological Analyses Of the London Metro Networkmentioning

confidence: 99%

A network-based framework for assessing infrastructure resilience: a case study of the London metro system

Chopra

Dillon

Bilec

et al. 2016

J. R. Soc. Interface.

128

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Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. It is imperative to build resilience of large-scale infrastructures like metro systems for addressing the threat of natural disasters and man-made attacks in urban areas. Analysis is needed to ensure that these systems are capable of withstanding and containing unexpected perturbations, and develop heuristic strategies for guiding the design of more resilient networks in the future. We present a comprehensive, multi-pronged framework that analyses information on network topology, spatial organization and passenger flow to understand the resilience of the London metro system. Topology of the London metro system is not fault tolerant in terms of maintaining connectivity at the periphery of the network since it does not exhibit small-world properties. The passenger strength distribution follows a power law, suggesting that while the London metro system is robust to random failures, it is vulnerable to disruptions on a few critical stations. The analysis further identifies particular sources of structural and functional vulnerabilities that need to be mitigated for improving the resilience of the London metro network. The insights from our framework provide useful strategies to build resilience for both existing and upcoming metro systems.

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Section: Topological Analyses Of the London Metro Networkmentioning

confidence: 99%

A network-based framework for assessing infrastructure resilience: a case study of the London metro system

Chopra

Dillon

Bilec

et al. 2016

J. R. Soc. Interface.

128

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“…Weighted Assortativity Coefficient: The weighted assortativity coefficient R W suggested by Leung et al [25] is given by :…”

Section: Network Measuresmentioning

confidence: 99%

Structure Comparison of Binary and Weighted Niche-Overlap Graphs

et al. 2014

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Abstract. In ecological networks, niche-overlap graphs are considered as complex systems. They represent the competition between two predators that share common resources. The purpose of this paper is to investigate the structural properties of these graphs considered as weighted networks and compare their measures with the ones calculated for the binary networks. To conduct this study, we select four classical network measures : the degree of nodes, the clustering coefficient, the assortativity, and the betweenness centrality. These measures were used to analyse different type of networks such as social networks, biological networks, world wide web, etc. Interestingly, we identify significant differences between the structure of the binary and the weighted niche-overlap graphs. This study indicates that weight information reveals different features that may provide other implications on the dynamics of these networks.

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“…The assortativity property of networks measures the preference of network nodes to attach to other nodes that are similar in terms of degree or strength where the latter is applicable for weighted networks Newman (2002), Leung and Chau (2007), Xie et al (2007). As the models proposed in this work generate weighted networks, we use the average nearest neighbor strength measure for this purpose.…”

Section: Network Assortativitymentioning

confidence: 99%

On the prevalence of hierarchies in social networks

Ranjbar-Sahraei

Ammar

Tuyls

et al. 2016

Soc. Netw. Anal. Min.

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In this paper, we introduce two novel evolutionary processes for hierarchical networks referred to as dominance-and prestige-based evolution models, i.e., DBEM and PBEM, respectively. Our models are deterministic in nature which allows for closed-form derivation of equilibrium points for such type of networks, for the special case of complete networks. After deriving these equilibrium points, we are somewhat surprised in recovering the exponential and power-law strength distribution as the shared property of the resulting hierarchal networks. Additionally, we compute the network properties, Geodesic distance distribution and centrality closeness, for each model in closed form. Interestingly, these results demonstrate very different roles of hubs for each model, shedding the light on the evolutionary advantages of hierarchies in social networks: in short, hierarchies can lead to efficient sharing of resources and robustness to random failures. For the general case of any hierarchical network, we compare the estimations of tie intensities and node strengths using the proposed models to open-source real-world data. The prediction results are statistically compared using the Kolmogorov-Smirnov test with the original data.

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Weighted assortative and disassortative networks model

Cited by 107 publications

References 29 publications

A network-based framework for assessing infrastructure resilience: a case study of the London metro system

A network-based framework for assessing infrastructure resilience: a case study of the London metro system

Structure Comparison of Binary and Weighted Niche-Overlap Graphs

On the prevalence of hierarchies in social networks

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