IEEE

Multi-Frequency Aware Reconstruction Model For Unsupervised Graph Anomaly Detection

Unsupervised graph anomaly detection (UGAD) is defined as identifying abnormal nodes that deviate from the normal distribution in a graph without any labels, and is widely used in financial fraud ...
IEEE

Graph Frequency-Domain Factor Modeling

Abstract: We propose a novel factor model in the graph frequency domain for multivariate data residing on the vertices of a graph, referred to as a multivariate graph signal. By utilizing graph ...
GitHub

Microsoft Graph Training Module - Build ASP.NET Core apps with Microsoft Graph

This module will introduce you to working with the Microsoft Graph .NET SDK in creating an ASP.NET Core MVC web application to access data in Office 365. Lab - Build ASP.NET Core apps with Microsoft ...
Frontiers

Research on intelligent diagnosis of mechanical rolling bearing faults through transfer learning

The proposed algorithm enhances the traditional conventional convolutional neural network (CNN) algorithm by introducing a domain category judgment module and an inter-domain conditional probability ...