LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

Outlier Detection Using Replicator Neural Networks

We consider the problem of finding outliers in large multivariate databases. Outlier detection can be applied during the data cleansing process of data mining to identify problems with the data itself, and to fraud detection where groups of outliers are often of particular interest. We use replicator neural networks (RNNs) to provide a measure of the outlyingness of data records. The performanc...

Outlier Detection using Graph Mining

Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks

Numerous applications in dynamic social networks, ranging from telecommunications to financial transactions, create evolving datasets. Detecting outliers in such dynamic networks is inherently challenging, because the arbitrary linkage structure with massive information is changing over time. Little research has been done on detecting outliers for dynamic social networks, even then, they repres...

Local Outlier Detection with Interpretation

Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that exp...

Graph Convolutional Neural Networks via Scattering

We generalize the scattering transform to graphs and consequently construct a convolutional neural network on graphs. We show that under certain conditions, any feature generated by such a network is approximately invariant to permutations and stable to graph manipulations. Numerical results demonstrate competitive performance on relevant datasets.