Parallelism in simulation and modeling of scale-free complex networks

Hrúz, Tomás; Geisseler, Stefan; Schöngens, Marcel

doi:10.1016/j.parco.2010.04.004

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Ren and Li [20] describe the simulation of a particular linear PA model, RX, but do not address the general problem of simulating networks from models with general preference functions. Hruz et al [21] and D' Angelo and Ferreti [22] provide methods for parallelizing the simulation of linear PA but do not treat the nonlinear case. Machta and Machta [23] analyze the general case for the PRAM shared-memory parallel architecture.…”

Section: Discussionmentioning

confidence: 99%

Efficient network generation under general preferential attachment

Atwood

Ribeiro

Towsley

2015

Compu Social Networls

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Preferential attachment (PA) models of network structure are widely used due to their explanatory power and conceptual simplicity. PA models are able to account for the scale-free degree distributions observed in many real-world large networks by sequentially introducing nodes that attach preferentially to existing nodes with high degree. The ability to efficiently generate instances from PA models is a key asset in understanding both the models themselves and the real networks that they represent. Surprisingly, little attention has been paid to the problem of efficient instance generation. In this paper, we show that the complexity of generating network instances from a PA model depends on the preference function of the model, provides efficient data structures that work under any preference function, and presents empirical results from an implementation based on these data structures. We demonstrate that, by indexing growing networks with a simple augmented heap, we can implement a network generator which scales many orders of magnitude beyond existing capabilities (10 6 to 10 8 nodes). We show the utility of an efficient and general PA network generator by investigating the consequences of varying the preference functions of an existing model. We also provide 'quicknet,' a freely available open-source implementation of the methods described in this work.

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Section: Discussionmentioning

confidence: 99%

Efficient network generation under general preferential attachment

Atwood

Ribeiro

Towsley

2015

Compu Social Networls

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“…For example, scale-free networks containing tens of thousands of network nodes often exhibit less than hundredfold parallelism. Finally, other work examines parallelism in the generation of large scale-free networks [Hruz et al 2010;Yoo and Henderson 2010] and use of hybrid synchronization approaches using a combination of local and global synchronization techniques [Liu and Rong 2012].…”

Section: Large-scale Parallel Discrete Event Simulations Of Complexmentioning

confidence: 99%

Research Challenges in Parallel and Distributed Simulation

Fujimoto

2016

ACM Trans. Model. Comput. Simul.

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The parallel and distributed simulation field has evolved and grown from its origins in the 1970s and 1980s and remains an active field of research to this day. A brief overview of research in the field is presented. Future research topics are explored including areas such as problem-driven simulation of large-scale systems and complex networks, exploitation of graphical processing unit hardware and cloud computing environments, predictive online simulation for system management and optimization, power and energy consumption in mobile platforms and data centers, and composition of heterogeneous simulations.

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“…Ren and Li [18] describe the simulation of a particular linear PA model, RX, but do not address the general problem of simulating networks from models with general preference functions. Hruz et al [13] and D'Angelo and Ferreti [10] provide methods for parallelizing the simulation of linear PA, but do not treat the nonlinear case. To the best of our knowledge, our work is the first to address the efficient generation from PA models under possibly nonlinear preference functions.…”

Section: Related Workmentioning

confidence: 99%

Efficient network generation under general preferential attachment

Atwood

Ribeiro

Towsley

2014

Proceedings of the 23rd International Conference on World Wide Web

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Preferential attachment (PA) models of network structure are widely used due to their explanatory power and conceptual simplicity. PA models are able to account for the scale-free degree distributions observed in many real-world large networks through the remarkably simple mechanism of sequentially introducing nodes that attach preferentially to high-degree nodes. The ability to efficiently generate instances from PA models is a key asset in understanding both the models themselves and the real networks that they represent. Surprisingly, little attention has been paid to the problem of efficient instance generation. In this paper, we show that the complexity of generating network instances from a PA model depends on the preference function of the model, provide efficient data structures that work under any preference function, and present empirical results from an implementation based on these data structures. We demonstrate that, by indexing growing networks with a simple augmented heap, we can implement a network generator which scales many orders of magnitude beyond existing capabilities (10 6 -10 8 nodes). We show the utility of an efficient and general PA network generator by investigating the consequences of varying the preference functions of an existing model. We also provide "quicknet", a freely-available open-source implementation of the methods described in this work.

show abstract

Parallelism in simulation and modeling of scale-free complex networks

Cited by 8 publications

References 20 publications

Efficient network generation under general preferential attachment

Efficient network generation under general preferential attachment

Research Challenges in Parallel and Distributed Simulation

Efficient network generation under general preferential attachment

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