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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.05254 |
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| _version_ | 1866929257272311808 |
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| author | Dadauto, Caio Vinicius da Fonseca, Nelson Luis Saldanha Torres, Ricardo da Silva |
| author_facet | Dadauto, Caio Vinicius da Fonseca, Nelson Luis Saldanha Torres, Ricardo da Silva |
| contents | Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication networks, emphasizing the need for advanced topology generators encompassing diverse Internet topology characteristics. This paper introduces a novel deep-learning-based generator of synthetic graphs representing intra-autonomous in the Internet, named Deep-Generative Graphs for the Internet (DGGI). It also presents a novel massive dataset of real intra-AS graphs extracted from the project ITDK, called IGraphs. It is shown that DGGI creates synthetic graphs that accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the MMD metric $84.4\%$, $95.1\%$, $97.9\%$, and $94.7\%$ for assortativity, betweenness, clustering, and node degree, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05254 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven Intra-Autonomous Systems Graph Generator Dadauto, Caio Vinicius da Fonseca, Nelson Luis Saldanha Torres, Ricardo da Silva Networking and Internet Architecture Machine Learning Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication networks, emphasizing the need for advanced topology generators encompassing diverse Internet topology characteristics. This paper introduces a novel deep-learning-based generator of synthetic graphs representing intra-autonomous in the Internet, named Deep-Generative Graphs for the Internet (DGGI). It also presents a novel massive dataset of real intra-AS graphs extracted from the project ITDK, called IGraphs. It is shown that DGGI creates synthetic graphs that accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the MMD metric $84.4\%$, $95.1\%$, $97.9\%$, and $94.7\%$ for assortativity, betweenness, clustering, and node degree, respectively. |
| title | Data-driven Intra-Autonomous Systems Graph Generator |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2308.05254 |