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Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks

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

Abstract

We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goal is to estimate this global centrality measure having at disposal a limited amount of data. This is the case in many real-world scenarios where data collection is expensive, the network is too big for data storage capacity or only partial information is available. The sampling algorithm is theoretically grounded by results derived from spectral approximation theory. We studied the problem on both synthetic and real data and tested the performance comparing with state-of-the-art methods. We show that approximations obtained from such methods are not always reliable and that our algorithm, while preserving computational scalability, improves performance under some relevant error measures.

Keywords

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Notes

  1. 1.

    http://irl.cs.ucla.edu/topology/.

  2. 2.

    https://snap.stanford.edu/data/ca-CondMat.html.

  3. 3.

    https://snap.stanford.edu/data/soc-Epinions1.html.

  4. 4.

    https://snap.stanford.edu/data/web-Stanford.html.

  5. 5.

    https://github.com/cdebacco/tcec_sampling.

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Correspondence to Caterina De Bacco .

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Ruggeri, N., De Bacco, C. (2020). Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks. 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_8

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