CENTRALITY ESTIMATION IN LARGE NETWORKS

ULRIK BRANDES, CHRISTIAN PICH
<span title="">2007</span> <i title="World Scientific Pub Co Pte Lt"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fdwi2lzrxzbppibh65sn47xgna" style="color: black;">International Journal of Bifurcation and Chaos in Applied Sciences and Engineering</a> </i> &nbsp;
Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the
more &raquo; ... lity of such estimates under various selection strategies for the source vertices.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1142/s0218127407018403">doi:10.1142/s0218127407018403</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jhietih2efgvji7y5gfs7k725i">fatcat:jhietih2efgvji7y5gfs7k725i</a> </span>
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