Intelligent Systems

Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks

2019

Article

pio


We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goalis to estimate this global centrality measure having at disposal a limited amount of data. This is the case inmany real-world scenarios where data collection is expensive, the network is too big for data storage capacityor only partial information is available. The sampling algorithm is theoretically grounded by results derivedfrom spectral approximation theory. We studied the problemon both synthetic and real data and tested theperformance comparing with traditional methods, such as random walk and uniform sampling. We show thatapproximations obtained from such methods are not always reliable and that our algorithm, while preservingcomputational scalability, improves performance under different error measures.

Author(s): Ruggeri, Nicolò and De Bacco, Caterina
Journal: International Conference on Complex Networks and Their Applications
Year: 2019
Month: November

Department(s): Physics for Inference and Optimization
Bibtex Type: Article (article)
Paper Type: Conference

DOI: https://doi.org/10.1007/978-3-030-36687-2_8
State: Published

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BibTex

@article{tcec,
  title = {Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks},
  author = {Ruggeri, Nicolò and De Bacco, Caterina},
  journal = {International Conference on Complex Networks and Their Applications},
  month = nov,
  year = {2019},
  doi = {https://doi.org/10.1007/978-3-030-36687-2_8},
  month_numeric = {11}
}