微信服务号

微信订阅号

2025-4-16- 9
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 [23]
    • |
  • Related [20]
    • | | |
  • 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
    [1] Rao J, Wu B, Dong YX. Parallel link prediction in complex network using mapreduce. Ruan Jian Xue Bao/Journal of Software, 2012,23(12):3175-3186(in Chinese with English abstract). http://wwww.jos.org.cn/1000-9825/4206.htm[doi:10.3724/SP.J.1001. 2012.04206]
    [2] Niu XZ, Niu JJ, Su DZ, She K. Community detection based on weighted content-structural network and random walks. Acta Electronica Sinica, 2017,45(9):2135-2142(in Chinese with English abstract).[doi:10.3969/j.issn.0372-2112.2017.09.012]
    [3] Hu WB, Wang H, Yan LP, Qiu ZY, Xiao L, Du B. Hybrid quantum swarm intelligence indexing for event detection in social networks. Ruan Jian Xue Bao/Journal of Software, 2016,27(11):2747-2762(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4910.htm[doi:10.13328/j.cnki.jos.004910]
    [4] Lu J, Gong Z, Lin X. A novel and fast SimRank algorithm. IEEE Trans. on Knowledge & Data Engineering, 2017,29(3):572-585.[doi:10.1109/TKDE.2016.2626282]
    [5] Liu Q, Li J, Xie Z, Zhang PY. An improvement of link prediction by combining local information and betweenness. In:Proc. of the 11th Int'l Conf. on Natural Computation (ICNC). Zhangjiajie, 2016. 456-461.[doi:10.1109/ICNC.2015.7378032]
    [6] Liu J. Analysis of link prediction method in mobile social network. In:Proc. of the 7th Int'l Conf. on Measuring Technology and Mechatronics Automation. Nanchang, 2015. 145-148.[doi:10.1109/ICMTMA.2015.41]
    [7] Sharma S, Singh A. An efficient method for link prediction in complex multiplex networks. In:Proc. of the 11th Int'l Conf. on Signal-Image Technology & Internet-Based Systems (SITIS). Bangkok, 2016. 453-459.[doi:10.1109/SITIS.2015.93]
    [8] Huang D, Zhang S, Hui P, Chen Z. Link pattern prediction in opportunistic networks with kernel regression. In:Proc. of the 7th Int'l Conf. on Communication Systems and Networks. Bangalore, 2015. 1-8.[doi:10.1109/COMSNETS.2015.7098684]
    [9] Liu MM, Guo JF, Luo X. Link prediction based on the similarity of transmission nodes of multiple paths in weighted social networks. Journal of Information Hiding and Multimedia Signal Processing, 2016,7(4):771-780.
    [10] Dai C, Chen L, Li B, LI Y. Link prediction in multi-relational networks based on relational similarity. Information Sciences, 2017,394-395:198-216.[doi:10.1016/j.ins.2017.02.003]
    [11] Yao L, Man Y, Huang Z, Deng J, Wang X. Secure routing based on social similarity in opportunistic networks. IEEE Trans. on Wireless Communications, 2016,15(1):594-605.[doi:10.1109/TWC.2015.2476466]
    [12] Li F, Si YL, Chen Z, Shen LM. Lightweight opportunistic routing forwarding strategy based on Markov chain. Journal on Communications, 2017,38(5):108-120(in Chinese with English abstract).[doi:10.11959/j.issn.1000-436x.2017105]
    [13] Rasul K, Makaroff D, Stanley KG. Hybrid community-based forwarding:A complete energy efficient algorithm for pocket switched networks. In:Proc. of the 40th IEEE Local Computer Networks Conference Workshops (LCN Workshops). Clearwater Beach, 2015. 99-100.[doi:10.1109/LCNW.2015.7365925]
    [14] Mei A, Morabito G, Santi P, Stefa J. Social-Aware stateless routingin pocket switched networks. IEEE Trans. on Parallel & Distributed Systems, 2015,26(1):252-261.[doi:10.1109/TPDS.2014.2307857]
    [15] Rhee I, Shin M, Hong S, et al. On the levy-walk nature of human mobility. IEEE/ACM Trans. on Networking, 2011,19(3):630-643.[doi:10.1109/TNET.2011.2120618]
    [16] Lin Y, Chen Z, Wu J. Efficient forwarding strategy for opportunistic network based on node similarity. Theoretical Computer Science, 2017,768:86-100.[doi:10.1007/978-981-10-6893-5_7]
    [17] Liu Y, Yang F, Zhang S, Zhou W. Impact of social features on the performance of pocket switched network. In:Proc. of the 79th IEEE Vehicular Technology Conf. (VTC Spring). Seoul, 2015. 1-5.[doi:10.1109/VTCSpring.2014.7022940]
    [18] Huang J, Cheng X, Bi J, Chen B. Wireless relay selection in pocket switched networks based on spatial regularity of human mobility. Sensors, 2016,16(1):94.[doi:10.3390/s16010094]
    附中文参考文献
    [1] 饶君,吴斌,东昱晓.MapReduce环境下的并行复杂网络链路预测.软件学报,2012,23(12):3175-3186. http://www.jos.org.cn/1000-9825/4206.htm[doi:10.3724/SP.J.1001.2012.04206]
    [2] 牛新征,牛嘉郡,苏大壮,佘堃.基于加权内容-结构网络和随机游走的社团划分算法.电子学报,2017,45(9):2135-2142.[doi:10. 3969/j.issn.0372-2112.2017.09.012]
    [3] 胡文斌,王欢,严丽平,邱振宇,肖雷,杜博.混合指标量子群智能社会网络事件检测方法.软件学报,2016,27(11):2747-2762. http://www.jos.org.cn/1000-9825/4910.htm.[doi:10.13328/j.cnki.jos.004910]
    [12] 李峰,司亚利,陈真,申利民.基于马尔可夫链的轻量级机会路由转发策略.通信学报,2017,38(5):108-120.[doi:10.11959/j. issn.1000-436x.2017105]
    Cited by
    Comments
    Comments
    分享到微博
    Submit
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:2172
  • PDF: 3357
  • HTML: 0
  • Cited by: 0
History
  • Received:May 01,2018
  • Online: November 13,2018
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the first2035273Visitors
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