S-BITE: A Structure-Based Internet Topology gEnerator

Accongiagioco, Giovanni; Gregori, Enrico; Lenzini, Luciano

doi:10.1016/j.comnet.2014.11.007

Cited by 6 publications

(21 citation statements)

References 32 publications

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“…Extensive analysis of empirical network data, which was necessary for the design of existing network generators, is replaced by inference of the model parameters, for which principled methods exist. As a result, our approach is applicable to arbitrary graphs, contrary to most of the existing generators [3], [6]- [8], [19]. Synthetic graphs can be generated by sampling from the inferred distribution.…”

Section: B Generation Of Synthetic Graphsmentioning

confidence: 99%

“…INET [19] is similar to BRITE and also combines structural and degree information. S-BITE [6] separates the Internet graph into different levels, models the connectivity between and within the levels and uses specific degree distributions, as well as preferential attachment and small world mechanisms. In contrast, Orbis [5] generates graphs according to a given (joint-)degree distribution.…”

Section: B Generation Of Synthetic Graphsmentioning

confidence: 99%

“…Examples are preferential attachment [1]- [3] models or generators replicating the degree distribution [4], [5] or matching structural patterns [6]- [8]. However, many network generators replicate only a subset of the original graphs' properties [6], [9]. In addition, communication networks change over time or develop new characteristics, which makes a continuous and extensive analysis necessary.…”

Section: Introductionmentioning

confidence: 99%

“…• Empirical graphs of different networking domains show and may combine different structural properties [6].…”

Section: Introductionmentioning

confidence: 99%

“…Many models and generators that produce graphs with properties similar to communication networks, mainly the Internet topology, have been proposed. Examples are preferential attachment [1]- [3] models or generators replicating the degree distribution [4], [5] or matching structural patterns [6]- [8]. However, many network generators replicate only a subset of the original graphs' properties [6], [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Generating synthetic Internet- and IP-topologies using the Stochastic-Block-Model

Kalmbach

Blenk

Kluegel

et al. 2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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Abstract-Developing models to generate realistic graphs of communication networks often requires a deep understanding and extensive analysis of the underlying network structure. Since deployed communication networks are dynamic, the findings a generator is based on might lose validity. We alleviate the need for extensive analysis of graphs by estimating parameters of a probabilistic model. The model parameters encode the structure of the graph, which is thus learned in an unsupervised fashion. Synthetic graphs can be generated from the model and will have the structure previously inferred. For this, we use the Stochastic-Block-Model (SBM) and the Degree-Corrected-BlockModel (DCBM), a variant allowing for heavy tailed degree distributions. The models originate in the social sciences and separate a graph into groups of nodes. To show the applicability of the models to the task of synthetic graph generation in the domain of communication networks, we use one router level and one IP-to-IP communication graph. We assert the quality of the generated models by evaluating a number of graph features and comparing our results to those obtained with the network generator Orbis. We find our approach to be on par with, or even outperforming Orbis. Furthermore, the models are able to capture large-scale structure in communication networks.

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Section: B Generation Of Synthetic Graphsmentioning

confidence: 99%

Section: B Generation Of Synthetic Graphsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…• Empirical graphs of different networking domains show and may combine different structural properties [6].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Generating synthetic Internet- and IP-topologies using the Stochastic-Block-Model

Kalmbach

Blenk

Kluegel

et al. 2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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Structural Decomposition Model for the Evolution of AS-Level Internet Topologies

Zhang

2020

IEEE Access

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Modeling Internet graphs at the autonomous-system (AS) level is helpful for recognizing and predicting the development trend of evolving Internet topology from a macro perspective. In contrast to the global statistical models such as the power-law distribution of node degrees, the structural decomposition models can more effectively represent the local connection. In this paper, we propose a structure-based model. Starting with the classification of links among the AS nodes, the proposed model partitions the core and periphery of Internet graphs into 16 atomic-level solid and dotted components. Additionally, the model captures the stable evolving features of these components based on the UCLA dataset that continuously explore Internet graphs over a long historic period from 2001 to 2015. Finally, according to the structure-based model, we design a new Internet-topology generator. Compared with the recently proposed generators, the advantages of our generator are as follows: (1) it accurately captures the structure decomposition property studied in this work, (2) it performs best on three statistical properties of the distance, assortativity coefficient, and maximum degree, and (3) it exhibits the best comprehensive performance in terms of runtime and multiple graph properties.

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Modeling Attacks in Computer Networks Using Boolean Constraint Propagation

Gorbatenko¹,

Семенов²

2018

2018 Global Smart Industry Conference (GloSIC)

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S-BITE: A Structure-Based Internet Topology gEnerator

Cited by 6 publications

References 32 publications

Generating synthetic Internet- and IP-topologies using the Stochastic-Block-Model

Generating synthetic Internet- and IP-topologies using the Stochastic-Block-Model

Structural Decomposition Model for the Evolution of AS-Level Internet Topologies

Modeling Attacks in Computer Networks Using Boolean Constraint Propagation

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