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Deterministic graph exploration for efficient graph sampling

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Abstract

Graph sampling is a widely used procedure in social network analysis, has attracted great interest in the scientific community and is considered as a very powerful and useful tool in several domains of network analysis. Apart from initial research in this area, which has proposed simple processes such as the classic Random Walk algorithm, Random Node and Random Edge sampling, during the last decade, more advanced graph sampling approaches have been emerged. In this paper, we extensively study the properties of a newly proposed method, the Rank Degree method, which leads to representative graph subgraphs. The Rank Degree is a novel graph exploration method which significantly differs from other existing methods in the literature. The novelty of the Rank Degree lies on the fact that its core methodology corresponds to a deterministic graph exploration; one specific variation corresponds to a number of parallel deterministic traverses that explore the graph. We perform extensive experiments on twelve real-world datasets of a different type, using a variety of measures and comparing our method with Forest Fire, Metropolis Hastings Random Walk and Metropolis Hastings. We provide strong evidence that our approach leads to highly efficient graph sampling; the generated samples preserve several graph properties, to a large extent.

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  1. https://drive.google.com/file/d/0BxezFIUuAvc3a29vY0Fqd3J5RUk.

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Acknowledgements

We thank Kyriaki Chryssaki for her helpful comments on the final manuscript. We also wish to acknowledge the Athens University of Economics and Business for the technical equipment provision.

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  1. Athens University of Economics and Business, 76, Patission Str., 10434, Athens, Greece

    Nikos Salamanos, Elli Voudigari & Emmanuel J. Yannakoudakis

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  1. Nikos Salamanos
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  2. Elli Voudigari
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  3. Emmanuel J. Yannakoudakis
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Correspondence to Nikos Salamanos.

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Salamanos, N., Voudigari, E. & Yannakoudakis, E.J. Deterministic graph exploration for efficient graph sampling. Soc. Netw. Anal. Min. 7, 24 (2017). https://doi.org/10.1007/s13278-017-0441-6

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