Model for generating artificial social networks having community structures with small-world and scale-free properties

Sallaberry, Arnaud; Zaidi, Faraz; Mélançon, Guy

doi:10.1007/s13278-013-0105-0

Cited by 43 publications

(34 citation statements)

References 40 publications

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“…Several decades later, Watts and Strogatz [16] and Newman and Watts [17] introduced models for generating smallworld networks. Typically, small-world networks have a relatively high clustering coefficient, and the distance between any two vertices scales as the logarithm of the number of vertices [18]. Barabási and Albert observed that degree distributions that follow power laws exist in a variety of networks, including the World Wide Web [19].…”

Section: Related Workmentioning

confidence: 99%

“…Even though the models described above can explain some of the characteristics of real-world complex networks, the random networks created by these models were lacking in other properties that were observed in real-world complex networks. Therefore, in recent years, other models have been suggested which have additional characteristics [16,18,22]. Thorough reviews on complex networks and complex network evolution models can be found in books by Chung and Lu [24], Newman et al [25], and by Dorogovtsev and Mendes [26].…”

Section: Related Workmentioning

confidence: 99%

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The rise and fall of network stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time

Fire

Guestrin

2020

Information Processing & Management

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Trends change rapidly in today's world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symbolized by vertices that "shine." That is, at a specific time interval in a network's life, certain vertices become increasingly connected to other vertices. This process creates new high-degree vertices, i.e., network stars. Thus, to study trends, we must look at how networks evolve over time and determine how the stars behave. In our research, we constructed the largest publicly available network evolution dataset to date, which contains 38,000 real-world networks and 2.5 million graphs. Then, we performed the first precise wide-scale analysis of the evolution of networks with various scales. Three primary observations resulted: (a) links are most prevalent among vertices that join a network at a similar time; (b) the rate that new vertices join a network is a central factor in molding a network's topology; and (c) the emergence of network stars (high-degree vertices) is correlated with fast-growing networks. We applied our learnings to develop a flexible network-generation model based on large-scale, real-world data. This model gives a better understanding of how stars rise and fall within networks, and is applicable to dynamic systems both in nature and society. Multimedia LinksVideo Interactive Data Visualization Data Code Tutorials

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The rise and fall of network stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time

Fire

Guestrin

2020

Information Processing & Management

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“…An exhaustive review of network generation models is out of scope in this text, yet we have tried to cite a wide spectrum of different network generation models. Partial surveys, reports and comparative analysis for different network generation models can be found in [12], [16], [25], [48], [54]- [57]. None of the models to generate networks considersboth demographic and structural attributes during the network generation process at the same time.…”

Section: Related Workmentioning

confidence: 99%

Generating online social networks based on socio-demographic attributes

Pasta

Zaidi

Rozenblat

2014

Journal of Complex Networks

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Recent years have seen tremendous growth of many online social networks such as Facebook, LinkedIn and MySpace. People connect to each other through these networks forming large social communities providing researchers rich datasets to understand, model and predict social interactions and behaviors. New contacts in these networks can be formed due to an individual's demographic attributes such as age group, gender, geographic location, or due to a network's structural dynamics such as triadic closure and preferential attachment, or a combination of both demographic and structural characteristics.A number of network generation models have been proposed in the last decade to explain the structure, evolution and processes taking place in different types of networks, and notably social networks. Network generation models studied in the literature primarily consider structural properties, and in some cases an individual's demographic profile in the formation of new social contacts. These models do not present a mechanism to combine both structural and demographic characteristics for the formation of new links. In this paper, we propose a new network generation algorithm which incorporates both these characteristics to model network formation. We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model. The proposed model is flexible and thus can generate networks with varying demographic and structural properties.

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“…For instance, currently investigations need more sophisticated models that include community structures because it changes the network dynamics. A review on this subject is found in [12]. Also, rewiring schemes need to be considered in the investigations because OSNs undergo changes in their links.…”

Section: Introductionmentioning

confidence: 99%

A Modular Architecture for the Development of Online Social Networks Simulators based on Chainable Processing Modules

Neto¹,

Paschoaletto²,

Angelis³

2017

JCIS

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Working with Complex Networks simulations focused on Online Social Networks (OSNs), we reached the limits of the architectural features of our software tool, because its project is not suitable to the inclusion of new resources as dynamic topological operations, on-thefly visualizations, recognition of yet-to-be-developed algorithms, and concurrency programming support. We analyzed our current simulator and prepared a requirement list target to the development of a new one, privileging two features: flexibility and extensibility. We developed and applied the concept of independent processing modules that can form a processing chain according to Design Patterns and Java interface standards. These Chainable Processing Modules are the novelty and foundation of our architecture, that complies very well with the new simulator requirements. In this work, we present our software architecture and highlight its potential to help the development of open projects of simulators of any type of Complex Networks.

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Model for generating artificial social networks having community structures with small-world and scale-free properties

Cited by 43 publications

References 40 publications

The rise and fall of network stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time

The rise and fall of network stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time

Generating online social networks based on socio-demographic attributes

A Modular Architecture for the Development of Online Social Networks Simulators based on Chainable Processing Modules

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