A weighted network evolving model with capacity constraints

Xiao-huan, WU; Zhu, Jiasong; Wu, Weiwei; Ge, Wei

doi:10.1007/s11433-013-5131-4

Cited by 9 publications

(11 citation statements)

References 32 publications

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“…In 2001, Bianconi and Barabasi [20] proposed a fitter-getricher modeling phenomenon, where probability of connection is directly proportional to the fitness value of a node. There are some more models based on different properties of nodes, such as intrinsic strength [21], competitiveness [22], initial attractiveness [23], or capacity constraints [24]. Jost and Joy [25] proposed an evolving model for social networks, where each person prefers to be friend with the friends of its friends.…”

Section: Related Workmentioning

confidence: 99%

Evolving models for meso-scale structures

Saxena

Iyengar

2016

2016 8th International Conference on Communication Systems and Networks (COMSNETS)

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Real world complex networks are scale free and possess meso-scale properties like core-periphery and community structure. We study evolution of the core over time in real world networks. This paper proposes evolving models for both unweighted and weighted scale free networks having local and global core-periphery as well as community structure. Network evolves using topological growth, self growth, and weight distribution function. To validate the correctness of proposed models, we use K-shell and S-shell decomposition methods. Simulation results show that the generated unweighted networks follow power law degree distribution with droop head and heavy tail. Similarly, generated weighted networks follow degree, strength, and edge-weight power law distributions. We further study other properties of complex networks, such as clustering coefficient, nearest neighbor degree, and strength degree correlation.

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Section: Related Workmentioning

confidence: 99%

Evolving models for meso-scale structures

Saxena

Iyengar

2016

2016 8th International Conference on Communication Systems and Networks (COMSNETS)

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“…Wu et al [198] presented a model based on the capacity constraint of the nodes. A node can not be connected with more than its capacity.…”

Section: Capacity Constraint Modelmentioning

confidence: 99%

A Survey of Evolving Models for Weighted Complex Networks based on their Dynamics and Evolution

Saxena¹

2020

Preprint

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For decades, complex networks, such as social networks, biological networks, chemical networks, technological networks, have been used to study the evolution and dynamics of different kinds of complex systems. These complex systems can be better described using weighted links as binary connections do not portray the complete information of the system. All these weighted networks evolve in a different environment by following different underlying mechanics. Researchers have worked on unraveling the evolving phenomenon of weighted networks to understand their structure and dynamics. In this chapter, we discuss the evolution of weighted networks and evolving models to generate different types of synthetic weighted networks, including undirected, directed, signed, multilayered, community, and core-periphery structured weighted networks. We further discuss various properties held by generated synthetic networks and their similarity with real-world weighted networks.

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“…Several novel models have been proposed in the literature [ 12 – 14 , 18 – 21 ]. For instances, in the model proposed by Baratt et al [ 14 ], the airports are distributed randomly in a disk, and the probability to construct a flight route from a specific airport to a destination increases linearly with its traffic and decreases exponentially with the distance between them.…”

Section: Introductionmentioning

confidence: 99%

Centrality anomalies for the domestic air transportation networks in the USA: an empirical benchmark

Sun

Zhu

2023

Eur. Phys. J. Plus

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Air transportation systems are a foundational infrastructure for the human’s society. The lack of systematic and detailed investigation on a large amount of records for air flights has blocked seriously the deep understanding of the systems. By using the American domestic passenger flight records from 1995 to 2020, we constructed the air transportation networks and calculated the betweenness and the eigenvector centralities for the airports. It is found that in terms of eigenvector centrality, 15–30% airports in the unweighted and undirected networks behave anomalous. The anomalies disappear after considering the information of link weights or directionalites. Five widely used models for air transportation networks are evaluated, results for which tell us that the spatial constraints are required to eliminate the anomalies detected by the eigenvector centrality, and provide us some references for selecting the parameters in the models. We hope the empirical benchmarks reported in this paper can stimulate much more works on theoretical models for air transportation systems.

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A weighted network evolving model with capacity constraints

Cited by 9 publications

References 32 publications

Evolving models for meso-scale structures

Evolving models for meso-scale structures

A Survey of Evolving Models for Weighted Complex Networks based on their Dynamics and Evolution

Centrality anomalies for the domestic air transportation networks in the USA: an empirical benchmark

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