论文标题
多个网络嵌入用于时间序列的异常检测
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
论文作者
论文摘要
本文考虑了图形时间序列中异常检测的图形信号处理问题。我们检查了两个相关的互补推理任务：时间序列中异常图的检测以及时间异常的顶点的检测。我们通过适应关节图推断的统计原则方法，特别是\ emph {多个邻接光谱嵌入}（MASE）来处理这些任务。我们证明我们的方法对我们的推理任务有效。此外，我们根据可检测到的异常的潜在性质评估方法的性能。我们进一步为我们的方法提供了理论上的理由，并洞悉其使用。我们的方法应用于安然通信图，大规模的商业搜索引擎时间序列以及幼虫果蝇连接组的数据，我们的方法证明了它们的适用性，并确定了远远超出大型变化的异常顶点。
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices. We approach these tasks via the adaptation of statistically principled methods for joint graph inference, specifically \emph{multiple adjacency spectral embedding} (MASE). We demonstrate that our method is effective for our inference tasks. Moreover, we assess the performance of our method in terms of the underlying nature of detectable anomalies. We further provide the theoretical justification for our method and insight into its use. Applied to the Enron communication graph, a large-scale commercial search engine time series of graphs, and a larval Drosophila connectome data, our approaches demonstrate their applicability and identify the anomalous vertices beyond just large degree change.
