# Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

arXiv: https://arxiv.org/abs/2012.14722 · HTML: https://mlsanity.com/paper/2012.14722/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Graph Attention Multi-Layer Perceptron](https://mlsanity.com/paper/2206.04355/) (2022)
- [Pretraining Language Models with Text-Attributed Heterogeneous Graphs](https://mlsanity.com/paper/2310.12580/) (2023)
- [Label-Enhanced Graph Neural Network for Semi-supervised Node Classification](https://mlsanity.com/paper/2205.15653/) (2022)
- [Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2202.09177/) (2022)
- [Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2210.00538/) (2022)
- [Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks](https://mlsanity.com/paper/2112.13078/) (2021)
- [Graph Attention MLP with Reliable Label Utilization](https://mlsanity.com/paper/2108.10097/) (2021)
- [HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2207.11887/) (2022)
- [RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs](https://mlsanity.com/paper/2211.11752/) (2022)

## Abstract (arXiv)

Heterogeneous graphs are pervasive in practical scenarios, where each graph consists of multiple types of nodes and edges. Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information. However, most of the existing graph convolution approaches were designed for homogeneous graphs, and therefore cannot handle heterogeneous graphs. Some recent methods designed for heterogeneous graphs are also faced with several issues, including the insufficient utilization of heterogeneous properties, structural information loss, and lack of interpretability. In this paper, we propose HGConv, a novel Heterogeneous Graph Convolution approach, to learn comprehensive node representations on heterogeneous graphs with a hybrid micro/macro level convolutional operation. Different from existing methods, HGConv could perform convolutions on the intrinsic structure of heterogeneous graphs directly at both micro and macro levels: A micro-level convolution to learn the importance of nodes within the same relation, and a macro-level convolution to distinguish the subtle difference across different relations. The hybrid strategy enables HGConv to fully leverage heterogeneous information with proper interpretability. Moreover, a weighted residual connection is designed to aggregate both inherent attributes and neighbor information of the focal node adaptively. Extensive experiments on various tasks demonstrate not only the superiority of HGConv over existing methods, but also the intuitive interpretability of our approach for graph analysis.