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Efficiently estimating node influence through group sampling over large graphs

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Abstract

The huge amount of graph data necessitates sampling methods to support graph-based analysis applications. Node influence is to count the influential nodes with a given node in large graphs that has wide applications including product promotion and information diffusion in social networks. However, existing sampling methods mainly consider node degree to compute the node influence while ignoring the important connections in terms of groups in which nodes participate, resulting in inaccuracy of influence estimations. To this end, this paper proposes group sampling, called GVRW, to count the groups along with node degrees to evaluate node influence in large graphs. Specifically, GVRW changes the way of random walker traversing a large graph from one node to a random neighbor node of the groups to enlarge the sampling space for the sake of characterizing the nodes and groups simultaneously. Furthermore, we carefully design the corresponding estimated method to employ the samples to estimate the specific distributions of groups and node degrees to compute the node influence. Experimental results on real-world graph datasets show that our proposed sampling and estimating methods can accurately obtain the properties and approximate the node influences closer to the real values than existing methods.

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No datasets were generated or analysed during the current study.

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Funding

This work is supported by NSFC(Natural Science Foundation of China) 62302043.

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Authors and Affiliations

  1. Information Engineering College, Capital Normal University, Beijing, China

    Lingling Zhang & Zhiping Shi

  2. School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China

    Zhiwei Zhang, Ye Yuan & Guoren Wang

Authors
  1. Lingling Zhang
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  2. Zhiping Shi
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  3. Zhiwei Zhang
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  4. Ye Yuan
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  5. Guoren Wang
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Contributions

Lingling Zhang: Conceptualization, Methodology, Software, Writing - original draft. Zhiping Shi: Conceptualization, Writing - original draft. Zhiwei Zhang and Ye Yuan: Supervision, Writing - review & editing. Guoren Wang: Writing - review & editing

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Correspondence to Lingling Zhang.

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Zhang, L., Shi, Z., Zhang, Z. et al. Efficiently estimating node influence through group sampling over large graphs. World Wide Web 27, 18 (2024). https://doi.org/10.1007/s11280-024-01257-4

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  • DOI: https://doi.org/10.1007/s11280-024-01257-4

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