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Empirical characterization of graph sampling algorithms

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Abstract

Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the properties of a given graph in the sampled graph. In this study, we provide a comprehensive empirical characterization of five graph sampling algorithms on six properties of a graph including degree, clustering coefficient, path length, global clustering coefficient, assortativity, and modularity. We extract samples from fifteen graphs grouped into five categories including collaboration, social, citation, technological, and synthetic graphs. We provide both qualitative and quantitative results. We find that there is no single method that extracts true samples from a given graph with respect to the properties tested in this work. Our results show that the sampling algorithm that aggressively explores the neighborhood of a sampled node performs better than the others.

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Data availability

The datsets used in the experiments are available publicly.

Code availability

The source code is available on request.

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Funding

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Authors and Affiliations

  1. Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan

    Muhammad Irfan Yousuf

  2. Department of Transportation Engineering and Management, University of Engineering and Technology, Lahore, Pakistan

    Izza Anwer

  3. Institute for Mathematics and Scientific Computing, Karl Franzens University, Graz, Austria

    Raheel Anwar

Authors
  1. Muhammad Irfan Yousuf
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  2. Izza Anwer
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  3. Raheel Anwar
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Contributions

MIY and RA proposed the main idea, implemented sampling algorithms and performed experiments. MIY and IA collected data, plot different graphs and tables. All the authors wrote their part. All authors reviewed the manuscript.

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Correspondence to Muhammad Irfan Yousuf.

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Yousuf, M.I., Anwer, I. & Anwar, R. Empirical characterization of graph sampling algorithms. Soc. Netw. Anal. Min. 13, 66 (2023). https://doi.org/10.1007/s13278-023-01060-5

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