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An adaptive graph sampling framework for graph analytics

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A Correction to this article was published on 09 February 2024

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Abstract

In large-scale data processing, graph analytics of complex interaction networks are indispensable. As the whole graph processing and analytics can be inefficient and usually impractical, graph sampling by keeping a portion of the original graph becomes a favorable approach. While prior work focused on fixed edge and node selection strategy based on predetermined criteria, without adaptive feedback to adjust the sampling process, this type of sampling algorithms has limited flexibility and estimation accuracy for complex graphs. In this paper, we propose an adaptive graph sampling framework, and design AdapES, an adaptive edge sampling algorithm based on this framework. Compared to non-adaptive sampling methods, our approach can continually monitor the difference between the current sampled subgraph and the original graph, and dynamically adjust the edge sampling probability based on this observed sampling difference. Guided by a preset sampling goal, this algorithm automatically adapts to the fluctuations in the random sampling process with high flexibility. The experimental evaluation in 11 datasets demonstrates that AdapES outperforms other algorithms for preserving various graph properties and statistics.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA

    Kewen Wang

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  1. Kewen Wang
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KW contributed to the work of conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review & editing, and visualization.

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Correspondence to Kewen Wang.

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The original online version of this arcle was revised: to update the missed corrections.

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Wang, K. An adaptive graph sampling framework for graph analytics. Soc. Netw. Anal. Min. 14, 4 (2024). https://doi.org/10.1007/s13278-023-01157-x

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