A new greedy sampling method, TCEC, uses spectral projection bounds to estimate eigenvector centrality rankings on incomplete networks and outperforms random-walk baselines on several real-world networks.
We define the border of a sampled subgraph as the set of all the incoming neighbors not include d in the sample itself
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Sampling on networks: estimating eigenvector centrality on incomplete graphs
A new greedy sampling method, TCEC, uses spectral projection bounds to estimate eigenvector centrality rankings on incomplete networks and outperforms random-walk baselines on several real-world networks.