论文标题
多重动态网络中的异常检测：从区块链安全到脑疾病预测
Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction
论文作者
论文摘要
在动态网络中识别异常的问题是具有广泛应用程序的基本任务。但是，由于异常的复杂本质，缺乏地面真理知识以及网络中复杂而动态的互动，它引起了关键的挑战。大多数现有方法通常研究网络在顶点之间具有单一连接的网络，而在许多应用程序中，对象之间的相互作用也会变化，从而产生多重网络。我们提出了一个多重动态网络的Anomuly，一个通用的，无监督的边缘异常检测框架。在每种关系类型中，Anomuly将节点嵌入在不同的GNN层处于分层节点状态，并采用GRU单元格来捕获网络的时间属性，并随着时间的推移更新节点嵌入。然后，我们添加了一种注意机制，该机制将各种关系的信息纳入了信息。我们对大脑网络的案例研究表明，如何将这种方法用作理解可能揭示脑部疾病或疾病的异常大脑活动的新工具。对九个现实世界数据集进行了广泛的实验表明，过度实现了最先进的性能。
The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications. However, it raises critical challenges due to the complex nature of anomalies, lack of ground truth knowledge, and complex and dynamic interactions in the network. Most existing approaches usually study networks with a single type of connection between vertices, while in many applications interactions between objects vary, yielding multiplex networks. We propose ANOMULY, a general, unsupervised edge anomaly detection framework for multiplex dynamic networks. In each relation type, ANOMULY sees node embeddings at different GNN layers as hierarchical node states and employs a GRU cell to capture temporal properties of the network and update node embeddings over time. We then add an attention mechanism that incorporates information across different types of relations. Our case study on brain networks shows how this approach could be employed as a new tool to understand abnormal brain activity that might reveal a brain disease or disorder. Extensive experiments on nine real-world datasets demonstrate that ANOMULY achieves state-of-the-art performance.
