Power-Law Behavior in Geometric Characteristics of Full Binary Trees
DC Field | Value | Language |
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dc.contributor.author | Paik, Kyungrock | - |
dc.contributor.author | Kumar, Praveen | - |
dc.date.accessioned | 2021-09-07T15:52:02Z | - |
dc.date.available | 2021-09-07T15:52:02Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2011-02 | - |
dc.identifier.issn | 0022-4715 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/113207 | - |
dc.description.abstract | Natural river networks exhibit regular scaling laws in their topological organization. Here, we investigate whether these scaling laws are unique characteristics of river networks or can be applicable to general binary tree networks. We generate numerous binary trees, ranging from purely ordered trees to completely random trees. For each generated binary tree, we analyze whether the tree exhibits any scaling property found in river networks, i.e., the power-laws in the size distribution, the length distribution, the distance-load relationship, and the power spectrum of width function. We found that partially random trees generated on the basis of two distinct types of deterministic trees, i.e., deterministic critical and supercritical trees, show contrasting characteristics. Partially random trees generated on the basis of deterministic critical trees exhibit all power-law characteristics investigated in this study with their fitted exponents close to the values observed in natural river networks over a wide range of random-degree. On the other hand, partially random trees generated on the basis of deterministic supercritical trees rarely follow scaling laws of river networks. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | FRACTAL DIMENSION | - |
dc.subject | NETWORKS | - |
dc.title | Power-Law Behavior in Geometric Characteristics of Full Binary Trees | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Paik, Kyungrock | - |
dc.identifier.doi | 10.1007/s10955-011-0125-y | - |
dc.identifier.scopusid | 2-s2.0-79951513031 | - |
dc.identifier.wosid | 000287251600010 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL PHYSICS, v.142, no.4, pp.862 - 878 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL PHYSICS | - |
dc.citation.title | JOURNAL OF STATISTICAL PHYSICS | - |
dc.citation.volume | 142 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 862 | - |
dc.citation.endPage | 878 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Physics, Mathematical | - |
dc.subject.keywordPlus | FRACTAL DIMENSION | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordAuthor | Self-similarity | - |
dc.subject.keywordAuthor | Binary tree | - |
dc.subject.keywordAuthor | Network topology | - |
dc.subject.keywordAuthor | Hack&apos | - |
dc.subject.keywordAuthor | s law | - |
dc.subject.keywordAuthor | Fractals | - |
dc.subject.keywordAuthor | Complex network | - |
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