Department of Information and Communication Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea.
Sensors (Basel). 2018 Nov 18;18(11):4020. doi: 10.3390/s18114020.
As graph stream data are continuously generated in Internet of Things (IoT) environments, many studies on the detection and analysis of changes in graphs have been conducted. In this paper, we propose a method that incrementally detects frequent subgraph patterns by using frequent subgraph pattern information generated in previous sliding window. To reduce the computation cost for subgraph patterns that occur consecutively in a graph stream, the proposed method determines whether subgraph patterns occur within a sliding window. In addition, subgraph patterns that are more meaningful can be detected by recognizing only the patterns that are connected to each other via edges as one pattern. In order to prove the superiority of the proposed method, various performance evaluations were conducted.
随着物联网 (IoT) 环境中不断产生图数据流，许多关于图变化检测和分析的研究已经展开。在本文中，我们提出了一种方法，该方法使用在前一个滑动窗口中生成的频繁子图模式信息来增量检测频繁子图模式。为了减少在图流中连续出现的子图模式的计算成本，所提出的方法确定子图模式是否在滑动窗口内出现。此外，通过仅将通过边相互连接的模式识别为一个模式，可以检测更有意义的子图模式。为了证明所提出方法的优越性，进行了各种性能评估。
