HGDL: Heterogeneous Graph Label Distribution Learning

Yufei Jin, Heng Lian, Yi He, Xingquan Zhu

Keywords: Heterogeneous Graph, Graph Representation Learning, Label Distribution Learning

Abstract:

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.

Primary Area: Graph neural networks

Submission Number: 13326