­Node classification in real-world attributed networks is a central analytical task that is a growing research area. In real-world networks, a large portion of node classes only contain limited labeled instances. Many prevailing graph machine learning methods typically rely upon the availability of sufficient labeled data. However, the long-tail...

Background Attributed networks provide high-impact data representation in many real-world complex systems, including social network analysis, financial fraud detection, and drug discovery. In these networks, users can be represented as nodes, which are classified based on the information available from the network. However, there are currently limited...

­Background In a variety of real-world applications (e.g., social spam detection, financial fraud detection, and network intrusion detection), detecting anomalies from networked data plays a vital role in keeping malicious behaviors or attacks at bay. With the increasing usage of attributed networks for modeling information systems, anomaly detection...

The state of the art of two-color focal plane arrays (FPAs) are two or three terminal photo detector pixels integrated with electronic readout circuits (ROICs). However, a multicolor (> 2) photodetector pixel requires many more terminals, making it very difficult, if not impossible, to integrate multicolor pixel arrays to ROICs due to the increase...