Large Scale Networks Fingerprinting and Visualization Using the K-Core Decomposition
Abstract
We use the k-core decomposition to develop algorithms for the analysis of large scale complex networks. This decomposition, based on a re- cursive pruning of the least connected vertices, allows to disentangle the hierarchical structure of networks by progressively focusing on their cen- tral cores. By using this strategy we develop a general visualization algo- rithm that can be used to compare the structural properties of various net- works and highlight their hierarchical structure. The low computational complexity of the algorithm, O(n + e), where n is the size of the net- work, and e is the number of edges, makes it suitable for the visualization of very large sparse networks. We show how the proposed visualization tool allows to find specific structural fingerprints of networks.
Cite
Text
Alvarez-hamelin et al. "Large Scale Networks Fingerprinting and Visualization Using the K-Core Decomposition." Neural Information Processing Systems, 2005.Markdown
[Alvarez-hamelin et al. "Large Scale Networks Fingerprinting and Visualization Using the K-Core Decomposition." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/alvarezhamelin2005neurips-large/)BibTeX
@inproceedings{alvarezhamelin2005neurips-large,
title = {{Large Scale Networks Fingerprinting and Visualization Using the K-Core Decomposition}},
author = {Alvarez-hamelin, J. I. and Dall'asta, Luca and Barrat, Alain and Vespignani, Alessandro},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {41-50},
url = {https://mlanthology.org/neurips/2005/alvarezhamelin2005neurips-large/}
}