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Spread Sampling and Its Applications on Graphs

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

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Abstract

Efficiently finding small samples with high diversity from large graphs has many practical applications such as community detection and online survey. This paper proposes a novel scalable node sampling algorithm for large graphs that can achieve better spread or diversity across communities intrinsic to the graph without requiring any costly pre-processing steps. The proposed method leverages a simple iterative sampling technique controlled by two parameters: infection rate, that controls the dynamics of the procedure and removal threshold that affects the end-of-procedure sampling size. We demonstrate that our method achieves very high community diversity with an extremely low sampling budget on both synthetic and real-world graphs, with either balanced or imbalanced communities. Additionally, we leverage the proposed technique for a very low sampling budget (only 2%) driven treatment assignment in Network A/B Testing scenario, and demonstrate competitive performance concerning baseline on both synthetic and real-world graphs.

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Notes

  1. 1.

    By design, our method achieves 100% expansion quality, a ratio of the neighborhood size of the sample to the number of unsampled nodes, as defined in [24] when the infection rate is exactly one node and removal threshold is one.

  2. 2.

    Evaluation is always performed on the ground truth communities.

  3. 3.

    The neighborhood inflation method [38] is also a highly cited work. We did not compare against it since according to [16], PPR performs better than the neighborhood inflation method.

  4. 4.

    Precisionrecall.

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Acknowledgments

This paper is funded by NSF grants DMS-1418265, IIS-1550302, and IIS-1629548.

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Authors and Affiliations

  1. The Ohio State University, Columbus, OH, 43210, USA

    Yu Wang, Bortik Bandyopadhyay, Vedang Patel, David Sivakoff & Srinivasan Parthasarathy

  2. Microsoft Inc., Hyderabad, India

    Aniket Chakrabarti

Authors
  1. Yu Wang
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  2. Bortik Bandyopadhyay
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  3. Vedang Patel
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  4. Aniket Chakrabarti
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  5. David Sivakoff
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  6. Srinivasan Parthasarathy
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Corresponding author

Correspondence to Yu Wang .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. UniversitĂ  degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Wang, Y., Bandyopadhyay, B., Patel, V., Chakrabarti, A., Sivakoff, D., Parthasarathy, S. (2020). Spread Sampling and Its Applications on Graphs. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_11

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