Rank-based deactivation model for networks with age

We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages： active and inactive. The transition from the active state to the inactive one is based on the rank of the node. In this paper, we adop...

Full description

Saved in:
Bibliographic Details
Published inChinese physics B Vol. 22; no. 1; pp. 578 - 582
Main Author 王学文 杨国宏 李小林 许新建
Format Journal Article
LanguageEnglish
Published 2013
Subjects
Age
Couples
Criteria
Deactivation
Evolution
Networks
Ranking
主动节点
基础
失活
年龄
模型
状态转换
网络演进
进化过程
Online AccessGet full text
ISSN1674-1056
2058-3834
1741-4199
DOI10.1088/1674-1056/22/1/018903

Cover

More Information
Summary:We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages： active and inactive. The transition from the active state to the inactive one is based on the rank of the node. In this paper, we adopt age as a criterion of ranking, and propose two deactivation models that generalize previous research. In model A, the older active node possesses the higher rank, whereas in model B, the younger active node takes the higher rank. We make a comparative study between the two models through the node-degree distribution.
Bibliography:complex networks, deactivation model, rank
11-5639/O4
We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages： active and inactive. The transition from the active state to the inactive one is based on the rank of the node. In this paper, we adopt age as a criterion of ranking, and propose two deactivation models that generalize previous research. In model A, the older active node possesses the higher rank, whereas in model B, the younger active node takes the higher rank. We make a comparative study between the two models through the node-degree distribution.
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1674-1056
2058-3834
1741-4199
DOI:10.1088/1674-1056/22/1/018903