A series of real networks invariants
arXiv:2601.0205219.51 citationsh-index: 5
AI Analysis
This work provides a new characterization of real networks for network scientists, but the results are incremental as they extend existing invariants.
The authors generalize two known network invariants (degree and ksi-centrality) by introducing a series of Laplacian-based centralities that exhibit exponential distributions for real networks and different distributions for artificial ones.
In this article we propose a generalization of two known invariants of real networks: degree and ksi-centrality. More precisely, we found a series of centralities based on Laplacian matrix, that have exponential distributions (power-law for the case $j = 0$) for real networks and different distributions for artificial ones.