Multimodal Graph Datasets for COVID-19 Forecasting using Geo-Social Media Signals
收藏DataCite Commons2026-05-14 更新2026-05-18 收录
下载链接：
https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/XZVO5I
下载链接
链接失效反馈官方服务：
资源简介：
This dataset contains the preprocessed graph datasets required to reproduce the results of the study:
"Comparative Evaluation of Geo-Social Media Signals in Multimodal Graph-Based Epidemiological Forecasting"
by Dorian Arifi, Devika Jain, Mauricio Santillana, and Bernd Resch.
The dataset includes multiple relational graph constructions, including spatial adjacency, mobility networks, semantic similarity, and random baseline structures. In addition, it provides node-level features such as COVID-19 case counts, geo-social media signals, and random time series used for controlled comparisons.
These data support the evaluation of multimodal Graph Neural Network (GNN) models for regional COVID-19 forecasting across different relational and feature configurations.
提供机构：
Harvard Dataverse
创建时间：
2026-04-14
