The emergence of mining complex networks like social media, sensor networks, and the world-wide-web has attracted considerable research interest. In a streaming scenario, the concept to be learned can change over time. However, while there has been some research done for detecting concept drift in traditional data streams, little work has been done on addressing concept drift in data represented as a graph . We propose a novel unsupervised concept-drift detection method on graph streams called Discriminative Subgraph-based Drift Detector (DSDD). The methodology starts by discovering discriminative subgraphs for each graph in the stream. We then compute the entropy of the window based on the distribution of discriminative subgraphs with respect to the graphs and then use the direct density-ratio estimation approach for detecting concept drift in the series of entropy values obtained by moving one step forward in the sliding window. The effectiveness of the proposed method is demonstrated through experiments using artificial and real-world datasets and its performance is evaluated by comparing against related baseline methods. Similarly, the usefulness of the proposed concept drift detection approach is studied by incorporating it in a popular graph stream classification algorithm and studying the impact of drift detection in classification accuracy.

中文翻译：

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一种使用判别子图的图流中概念漂移检测的方法

挖掘复杂网络（如社交媒体、传感器网络和万维网）的出现引起了相当大的研究兴趣。在流式传输场景中，要学习的概念会随着时间而改变。然而，虽然已经对检测传统数据流中的概念漂移进行了一些研究，但在解决表示为图形. 我们提出了一种新的基于图流的无监督概念漂移检测方法，称为基于判别子图的漂移检测器（DSDD）。该方法首先为流中的每个图发现判别子图。然后，我们根据判别子图相对于图的分布计算窗口的熵，然后使用直接密度比估计方法检测通过在滑动窗口中向前移动一步获得的一系列熵值中的概念漂移. 通过使用人工和真实世界数据集的实验证明了所提出方法的有效性，并通过与相关基线方法进行比较来评估其性能。相似地，