IEEE

Data Reconstruction via Consensus Graph Learning for Effective Anomaly Detection in Industrial IoT

Abstract: In the industrial Internet of Things (IIoT), anomaly detection is fundamental to ensuring system safety and product quality, among other things. However, the massive amount of unlabeled, ...
GitHub

Graph Neural Networks for Tabular Data Learning (GNN4TDL)

The deep learning-based approaches to Tabular Data Learning (TDL), classification and regression, have shown competing performance, compared to their conventional counterparts. However, the latent ...
IEEE

Distributed Graph Learning From Smooth Data: A Bayesian Framework

Abstract: The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in Graph Signal Processing (GSP) and finds applications in various data ...
GitHub

GraphStorm: Enterprise graph machine learning framework for billion-scale graphs

GraphStorm is an enterprise-grade graph machine learning (GML) framework designed for scalability and ease of use. It simplifies the development and deployment of GML models on industry-scale graphs ...