东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2013, Vol. 34 ›› Issue (9): 1232-1235.DOI: -

• 信息与控制 • 上一篇    下一篇

IP级拓扑新生与消亡节点的特征

赵海1，刘怡文2，艾均1，贾思媛1   

  1. (1东北大学信息科学与工程学院，辽宁沈阳110819；2东北大学网络中心，辽宁沈阳110819)
  • 收稿日期:2013-04-02 修回日期:2013-04-02 出版日期:2013-09-15 发布日期:2013-04-22
  • 通讯作者: 赵海
  • 作者简介:赵海(1959-)，男，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61101121).

Characteristics of Birth and Death Nodes with IPLevel Topology

ZHAO Hai1， LIU Yiwen2， AI Jun1， JIA Siyuan1   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China； 2. Network Center， Northeastern University， Shenyang 110819， China.
  • Received:2013-04-02 Revised:2013-04-02 Online:2013-09-15 Published:2013-04-22
  • Contact: LIU Yiwen
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摘要： 针对IP级拓扑新生与消亡节点特征进行研究，基于CAIDA项目授权的海量数据，结合复杂网络的研究理论和方法，对IP级Internet拓扑新生与消亡节点的度分布、幂律特性等特征量，以及IP级新生连接进行统计分析及演化分析.结果表明，新生与消亡节点近似符合幂律分布，新生节点不倾向于与新生节点相连，而倾向于与网络中已存在的节点相连，且连接数为1的可能性较大.新生连接不符合优先连接性质，富者更富理论在动态上的表现不显著，高度节点的影响力在连接产生时不高.新生节点连接数目具有稳定性及普适性，这为Internet建模提供有效依据.

关键词: 复杂网络, 互联网IP级, 新生节点, 消亡节点, 幂律分布, 优先连接, 演化分析

Abstract: Combining the theory and method of complex network， Internet IPlevel topology measured by CAIDA was used to study characteristics of birth and death nodes. Characteristics of birth and death nodes’ degree distribution and powerlaw distribution were obtained. In addition， statistic and evolution analysis of birth attachments were achieved. The results show that both birth and death nodes accord with powerlaw distribution approximately， and birth nodes do not tend to connect with birth nodes， but connect the nodes which have existed already in the network. The number of attachment is likely to be 1. Because birth attachments do not accord with preferential attachment， the theory of the rich becoming richer has little significance on dynamic topology and nodes with high degree do not bring great impact on birth nodes. Generating mechanism of attachment is obtained， and the number of attachments is stable and universal， which provides a valid basis for Internet modeling.

Key words: complex network, Internet IPlevel, birth node, death node, powerlaw distribution, preferential attachment, evolution analysis

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