• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Yang Guoqiang, Dou Qiang, and Dou Wenhua. dK Series Analysis on Annotated AS Topology Graph[J]. Journal of Computer Research and Development, 2010, 47(9): 1633-1642.
Citation: Yang Guoqiang, Dou Qiang, and Dou Wenhua. dK Series Analysis on Annotated AS Topology Graph[J]. Journal of Computer Research and Development, 2010, 47(9): 1633-1642.

dK Series Analysis on Annotated AS Topology Graph

More Information
  • Published Date: September 14, 2010
  • Topology property analysis and topology generation are important tasks for the Internet topology researchers. The topology properties of the Internet and the generated topologies with the same properties of the Internet topology are valuable for many other research fields, such as routing protocol designing, network performance analyzing and next generation network constructing. The dK series is proved to be an efficient tool for systematic topology property analysis, and the d=2 case is sufficient for most practical purposes. When using the dK series, the Internet topology is described as an undirected graph. Whereas, the AS(autonomous system) level Internet topology is better described by a graph annotated by AS relationships, as the complicated commercial relationships between ASs. In this paper, based on the definition of the dK series, a new series called dK′ series is proposed to analyze the property of the annotated AS topology graph, and a novel approach is presented to generate graphs with a given 2K′ distribution. The generation approach is based on an improved 2K graph generation algorithm which outperforms the existing 2K graph generation algorithm. By analyzing the result of experiment, it is found that the d=2 case is sufficient to describe most important properties of annotated AS graph.
  • Related Articles

    [1]Zhou Xiaohui, Wang Yijie, Xu Hongzuo, Liu Mingyu. Fusion Learning Based Unsupervised Anomaly Detection for Multi-Dimensional Time Series[J]. Journal of Computer Research and Development, 2023, 60(3): 496-508. DOI: 10.7544/issn1000-1239.202220490
    [2]Wang Ling, Zhou Nan, Shen Peng. Time Series Anomaly Pattern Recognition Based on Adaptive k Nearest Neighbor[J]. Journal of Computer Research and Development, 2023, 60(1): 125-139. DOI: 10.7544/issn1000-1239.202111062
    [3]Huang Xunhua, Zhang Fengbin, Fan Haoyi, Xi Liang. Multimodal Adversarial Learning Based Unsupervised Time Series Anomaly Detection[J]. Journal of Computer Research and Development, 2021, 58(8): 1655-1667. DOI: 10.7544/issn1000-1239.2021.20201037
    [4]Zhang Zhenguo, Wang Chao, Wen Yanlong, Yuan Xiaojie. Time Series Shapelets Extraction via Similarity Join[J]. Journal of Computer Research and Development, 2019, 56(3): 594-610. DOI: 10.7544/issn1000-1239.2019.20170741
    [5]Wu Honghua, Liu Guohua, Wang Wei. Similarity Matching for Uncertain Time Series[J]. Journal of Computer Research and Development, 2014, 51(8): 1802-1810. DOI: 10.7544/issn1000-1239.2014.20121055
    [6]Su Weixing, Zhu Yunlong, Liu Fang, Hu Kunyuan. Outliers and Change-Points Detection Algorithm for Time Series[J]. Journal of Computer Research and Development, 2014, 51(4): 781-788.
    [7]Cheng Wencong, Zou Peng, and Jia Yan. Similar Sub-Sequences Search over Multi-Dimensional Time Series Data[J]. Journal of Computer Research and Development, 2010, 47(3): 416-425.
    [8]Yang Guoqiang and Dou Wenhua. A Fast Algorithm for Inferring AS-Level Path of Internet Topology[J]. Journal of Computer Research and Development, 2009, 46(11): 1797-1802.
    [9]Duan Jiangjiao, Xue Yongsheng, Lin Ziyu, Wang Wei, Shi Baile. A Novel Hidden Markov Model-Based Hierarchical Time-Series Clustering Algorithm[J]. Journal of Computer Research and Development, 2006, 43(1): 61-67.
    [10]Xiao Hui and Hi Yunfa. Data Mining Based on Segmented Time Warping Distance in Time Series Database[J]. Journal of Computer Research and Development, 2005, 42(1): 72-78.

Catalog

    Article views (710) PDF downloads (410) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return