- author = "Marcus Maertens",
- title = "Information Propagation in Complex Networks Structures and Dynamics",
- school = "Delft University of Technology",
- year = "2018",
- address = "Holland",
- month = "8 " # jan,
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, information propagation, functional brain networks, toxicity, multi-player online games, network epidemics, epidemic spreading model, complex networks, symbolic regression",
- isbn13 = "978-94-028-0907-7",
-
URL = "
https://repository.tudelft.nl/islandora/object/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1",
-
URL = "https://repository.tudelft.nl/islandora/object/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1/datastream/OBJ/download",
-
DOI = "
10.4233/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1",
- size = "150 pages",
- abstract = "... chapter 6 is a study on the capabilities of symbolic regression for network properties. We develop an automated system based on Genetic Programming which is able to be trained by families of networks to learn the relations between several of their properties. These properties can be features of the networks like the eigenvalues of their adjacency or Laplacian matrices or network metrics like the network diameter or the isoperimetric number. We show that the system can generate approximate formulas for those metrics that often give better results than previously known analytic bounds. The evolved formulae for the network diameter are evaluated on a selection of real-world networks of different origins. The network diameter bounds hop-based information propagation and is thus of high importance for designing network algorithms. A careful selection of training networks and network features is crucial for evolving good approximate formulas for the network diameter and similar properties. ...",
-
notes = "Supervisor prof. dr. ir. P. F. A. Van Mieghem Section
6.2.2 Cartesian Genetic Programming
(CGP)
http://repository.tudelft.nl/",
