One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling
Ruoxue Ma, Jiarong Xu, Xinnong Zhang, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, zhongyu wei
Abstract
Online community is composed of communities, users, and user-generated textual content, with rich information that can help us solve social problems. Previous research hasn't fully utilized these three components and the relationship among them. What's more, they can't adapt to a wide range of downstream tasks. To solve these problems, we focus on a framework that simultaneously considers communities, users, and texts. And it can easily connect with a variety of downstream tasks related to social media. Specifically, we use a ternary heterogeneous graph to model online communities. Text reconstruction and edge generation are used to learn structural and semantic knowledge among communities, users, and texts. By leveraging this pre-trained model, we achieve promising results across multiple downstream tasks, such as violation detection, sentiment analysis, and community recommendation. Our exploration will improve online community modeling.
BibTeX
@inproceedings{
ma2023onemodelconnectsall,
title={One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling},
author={Ruoxue Ma and Jiarong Xu and Xinnong Zhang and Haozhe Zhang and Zuyu Zhao and Qi Zhang and Xuanjing Huang and zhongyu wei},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=3aF1Rv3dHG}
}