Information processing in complex networks: Graph entropy and information functionals

By Matthias Dehmer. Applied Mathematics and Computation, 2008

302 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3830a92f2f6766be3b44a982faec692c2032a2f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Fabián Riquelme
2. Lavanya Sivakumar
3. Pablo Gonzalez Cantergiani

In Molecular Biology

1. Lavanya Sivakumar
2. Fabián Riquelme
3. Pablo Gonzalez Cantergiani
4. Matthias Dehmer
5. Sergey V. Krivovichev

In Statistical and Nonlinear Physics

1. Lavanya Sivakumar
2. Fabián Riquelme
3. Pablo Gonzalez Cantergiani
4. Matthias Dehmer
5. Sergey V. Krivovichev

Citations in the corpus

• Uniquely Discriminating Molecular Structures Using Novel Eigenvalue-Based Descriptors (2012, methodology): “More technical details and other information functionals can be found in [12].”
• Measuring user influence on Twitter: A survey (2015, methodology): “After that, the most influential tweets are measured on different topics using degree centrality, shortest path-cost, and graph entropy , the latter based on Dehmer [28].”