IEEE

A Novel Discrete Time Series Representation with De Bruijn Graphs for Enhanced Forecasting Using TimesNet (Extended Abstract)

Abstract: This paper introduces a novel method for time series forecasting using de Bruijn Graphs (dBGs) to represent discretized time series data. Our approach involves (1) encoding time series as a ...
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 ...
GitHub

GraphGPT: Graph Instruction Tuning for Large Language Models

Data Intelligence Lab@University of Hong Kong, Baidu Inc. This repository hosts the code, data and model weight of GraphGPT (SIGIR'24 full paper track). Due to compatibility issues, if you are using ...