Show simple item record Hülse, Martin 2008-11-12T18:43:00Z 2008-11-12T18:43:00Z 2010-01
dc.identifier.citation Hülse , M 2010 , ' From Sierpinski Carpets to Directed Graphs ' Complex Systems , vol 19 , no. 1 , pp. 45-71 . en
dc.identifier.other PURE: 157114
dc.identifier.other PURE UUID: d6e5d3a3-5f7a-4186-8dac-910447255f15
dc.identifier.other dspace: 2160/6086
dc.identifier.other DSpace_20121128.csv: row: 3914
dc.identifier.other dspace: 2160/1095
dc.identifier.other DSpace_20121128.csv: row: 933
dc.identifier.uri en
dc.description Huelse, M.: From Sierpinski Carpets to Directed Graphs. Complex Systems, 19(1), 45-71, 2010. Sponsorship: EPSRC en
dc.description.abstract Models of complex information processing based on artificial neural networks frequently apply fully connected or random graph structures. However, it is well known that biological neural systems operate on sparsely connected networks having properties quite distinct to random graphs. In this paper, a simple method is introduced for the deterministic generation of strongly connected digraphs. The method is inspired by Sierpinski carpets. Despite the large size of these digraphs, the distance between most of the nodes is short, that is, it scales logarithmically. It is further shown that important network properties, such as average degree and degree distribution, can directly be determined by the initial structure of this process. These findings lead to the formulation of general conditions providing a targeted generation of complex networks of arbitrary size. The circumstances under which these digraphs can show scale-free and small-world properties are discussed and finally possible applications of this method are outlined in the domain of artificial neural networks. en
dc.format.extent 27 en
dc.language.iso eng
dc.relation.ispartof Complex Systems en
dc.rights en
dc.title From Sierpinski Carpets to Directed Graphs en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version preprint en
dc.description.version preprint en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Peer reviewed en

Files in this item

Aside from theses and in the absence of a specific licence document on an item page, all works in Cadair are accessible under the CC BY-NC-ND Licence. AU theses and dissertations held on Cadair are made available for the purposes of private study and non-commercial research and brief extracts may be reproduced under fair dealing for the purpose of criticism or review. If you have any queries in relation to the re-use of material on Cadair, contact

This item appears in the following Collection(s)

Show simple item record

Search Cadair

Advanced Search