微信服务号

微信订阅号

2025-4-13- 6
Home > Archive>Volume 29, Issue S1, 2018 >92-104
PDF HTML XML Export Cite reminder
Similarity Index Based on Node Behavior Patterns
Author:
Affiliation:

Fund Project:

National Natural Science Foundation of China (61762065, 61363015, 61262020); Natural Science Foundation of Jiangxi Province, China (20171ACB20018, 20171BAB202009, 20171BBH80022); Innovation Foundation for Postgraduate Student of Jiangxi Province (YC2018093)

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    Pocket switched networks (PSN) is a kind of delay tolerant network (DTN) which transmits messages based on node movement and opportunity encounter. The traditional similarity index based prediction methods are not suitable for the link prediction of PSN due to the frequent topology change and time-various link in PSN. According to the characteristics of PSN node behavior, this paper analyzes connection time, connection duration, and the law of node accessing areas. Node similarity is categorized into sociality behavior similarity and movement behavior similarity. After comparing AUC and Precision with different weight for sociality behavior similarity and movement behavior similarity, similarity index based on node behavior patterns (SNBP) is proposed. The experiment results on MIT Reality and Dartmouth Campus datasets show that comparison with CN, AA, and Katz similarity index, the proposed similarity index has better precision.

    Reference
    Related
    Cited by
Get Citation

舒坚,马玉良,林伟杰,刘琳岚.一种基于节点行为模式的相似性指标.软件学报,2018,29(S1):92-104

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:May 01,2018
  • Online: November 13,2018
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063