Abstract: Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman–Pearson criterion, the design of one-class classifiers boils down ...

Abstract: Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and ...

Description: The graph visualizer currently supports traversals. A great addition would be to implement a shortest path algorithm. This would involve allowing users to add weights to edges, select a ...