Graph measures with high discrimination power revisited: A random polynomial approach
Research output: Contribution to journal › Article › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Pages (from-to) | 407-414 |
| Number of pages | 8 |
| Journal | Information Sciences |
| Volume | 467 |
| DOIs | |
| Publication status | Published - 1 Oct 2018 |
| Publication type | A1 Journal article-refereed |
Abstract
Finding graph measures with high discrimination power has been triggered by searching for so-called complete graph invariants. In a series of papers, we have already investigated highly discriminating measures to distinguish graphs (networks) based on their topology. In this paper, we propose an approach where the graph measures are based on the roots of random graph polynomials. The polynomial coefficients have been defined by utilizing information functionals which capture structural information of the underlying networks. Our numerical results obtained by employing exhaustively generated graphs reveal that the new approach outperforms earlier results in the literature.
ASJC Scopus subject areas
Keywords
- Data science, Graphs, Networks, Quantitative graph theory, Statistics