Approximation of the Diagonal of a Laplacian's Pseudoinverse for Complex Network Analysis

@inproceedings{Angriman2020ApproximationOT,
  title={Approximation of the Diagonal of a Laplacian's Pseudoinverse for Complex Network Analysis},
  author={Eugenio Angriman and Maria Predari and Alexander van der Grinten and Henning Meyerhenke},
  booktitle={ESA},
  year={2020}
}
The ubiquity of massive graph data sets in numerous applications requires fast algorithms for extracting knowledge from these data. We are motivated here by three electrical measures for the analysis of large small-world graphs $G = (V, E)$ -- i.e., graphs with diameter in $O(\log |V|)$, which are abundant in complex network analysis. From a computational point of view, the three measures have in common that their crucial component is the diagonal of the graph Laplacian's pseudoinverse, $L… 
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