@inproceedings{
jin2024hgdl,
title={{HGDL}: Heterogeneous Graph Label Distribution Learning},
author={Yufei Jin and Heng Lian and Yi He and Xingquan Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OwguhIAh8R}
}
Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification. 
This paper advances LDL into graph domains and aims to tackle a novel and fundamental
heterogeneous graph label distribution learning (HGDL) problem.
We argue that 
the graph heterogeneity reflected on node types, node attributes, and  neighborhood structures can 
impose significant challenges for generalizing 
LDL onto graphs. 
To address the challenges, we propose a new 
learning framework with two key components: 
1) proactive graph topology homogenization, 
and 2) topology and content consistency-aware graph transformer. 
Specifically, 
the former learns optimal information aggregation between meta-paths, so that the node
heterogeneity can be proactively addressed prior to the succeeding embedding learning; the latter leverages an attention mechanism to learn consistency between meta-path and node attributes, allowing network topology and nodal attributes to be equally emphasized during the label distribution learning. By using KL-divergence and additional constraints, \method~delivers 
an end-to-end solution for learning and predicting label distribution for nodes. 
Both theoretical and empirical studies substantiate 
the effectiveness of our HGDL approach.
Our code and datasets are available at https://github.com/Listener-Watcher/HGDL.
HGDL: Heterogeneous Graph Label Distribution Learning
Yufei Jin Heng Lian Yi He Xingquan Zhu