| dc.contributor.author |
Piersa, Jarosław |
| dc.date.accessioned |
2014-02-08T17:43:37Z |
| dc.date.available |
2014-02-08T17:43:37Z |
| dc.date.issued |
2012 |
| dc.identifier.citation |
Lecture Notes in Computer Science, 7203, pp 511-520 |
| dc.identifier.issn |
0302-9743 |
| dc.identifier.uri |
http://repozytorium.umk.pl/handle/item/1673 |
| dc.description |
Full article is available at Springerlink:
http://link.springer.com/chapter/10.1007%2F978-3-642-31464-3_52
DOI: 10.1007/978-3-642-31464-3_52 |
| dc.description.abstract |
Average path length is recognised as one of the vital characteristics of random graphs and complex networks. Despite a rather sparse structure, some cases were reported to have a relatively short lengths between every pair of nodes, making the whole network available in just several hops. This small-worldliness was reported in metabolic, social or linguistic networks and recently in the Internet. In this paper we present results concerning path length distribution and the diameter of the spike-flow graph obtained from dynamics of geometrically embedded neural networks. Numerical results confirm both short diameter and average path length of resulting activity graph. In addition to numerical results, we also discuss means of running simulations in a concurrent environment. |
| dc.description.sponsorship |
The work was supported by Polish Ministry of Science and
grant project UMO-2011/01/N/ST6/01931.
The author is grateful to PL-Grid Project for providing a computing infrastructure for simulations. |
| dc.language.iso |
eng |
| dc.publisher |
Springer Berlin Heidelberg |
| dc.relation.ispartofseries |
Lecture Notes in Computer Science; |
| dc.rights |
info:eu-repo/semantics/openAccess |
| dc.subject |
geometrical neural networks |
| dc.subject |
path length distribution |
| dc.subject |
small world graph |
| dc.subject |
graph diameter |
| dc.title |
Diameter of the spike-flow graphs of geometrical neural networks |
| dc.type |
info:eu-repo/semantics/conferenceObject |
