# Interpretable and Efficient Heterogeneous Graph Convolutional Network

arXiv: https://arxiv.org/abs/2005.13183 · HTML: https://mlsanity.com/paper/2005.13183/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 30 of 48)

- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering](https://mlsanity.com/paper/2210.10462/) (2022)
- [Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks](https://mlsanity.com/paper/2207.06819/) (2022)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [A Meta-Learning Approach for Training Explainable Graph Neural Networks](https://mlsanity.com/paper/2109.09426/) (2021)
- [Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN](https://mlsanity.com/paper/2412.17629/) (2024)
- [SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks](https://mlsanity.com/paper/2112.11628/) (2021)
- [Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks](https://mlsanity.com/paper/2203.02018/) (2022)
- [Towards Explainable Motion Prediction using Heterogeneous Graph Representations](https://mlsanity.com/paper/2212.03806/) (2022)
- [Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks](https://mlsanity.com/paper/2302.14061/) (2023)
- [GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy](https://mlsanity.com/paper/2312.09708/) (2023)
- [Heterogeneous Graph Neural Network with Multi-view Representation Learning](https://mlsanity.com/paper/2108.13650/) (2021)
- [Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes](https://mlsanity.com/paper/2212.02055/) (2022)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs](https://mlsanity.com/paper/2401.03597/) (2024)
- [Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2312.15237/) (2023)
- [Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains](https://mlsanity.com/paper/2412.08937/) (2024)
- [Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks](https://mlsanity.com/paper/2112.13078/) (2021)
- [Few-Shot Semantic Relation Prediction across Heterogeneous Graphs](https://mlsanity.com/paper/2207.05068/) (2022)
- [MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2211.12792/) (2022)
- [Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks](https://mlsanity.com/paper/2202.03874/) (2022)
- [Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph](https://mlsanity.com/paper/2305.10771/) (2023)
- [G3SR: Global Graph Guided Session-based Recommendation](https://mlsanity.com/paper/2203.06467/) (2022)
- [GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs](https://mlsanity.com/paper/2208.06144/) (2022)
- [Pseudo Contrastive Learning for Graph-based Semi-supervised Learning](https://mlsanity.com/paper/2302.09532/) (2023)
- [HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs](https://mlsanity.com/paper/2404.00816/) (2024)
- [LSPI: Heterogeneous Graph Neural Network Classification Aggregation Algorithm Based on Size Neighbor Path Identification](https://mlsanity.com/paper/2405.18933/) (2024)
- [HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2407.13158/) (2024)
- [Aligning Multiple Knowledge Graphs in a Single Pass](https://mlsanity.com/paper/2408.00662/) (2024)
- [Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference](https://mlsanity.com/paper/2411.14035/) (2024)

## Abstract (arXiv)

Graph Convolutional Network (GCN) has achieved extraordinary success in learning effective task-specific representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for a target object, which hinders both effectiveness and interpretability; (2) they often need to generate intermediate meta-path based dense graphs, which leads to high computational complexity. To address the above issues, we propose an interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn the representations of objects in HINs. It is designed as a hierarchical aggregation architecture, i.e., object-level aggregation first, followed by type-level aggregation. The novel architecture can automatically extract useful meta-paths for each object from all possible meta-paths (within a length limit), which brings good model interpretability. It can also reduce the computational cost by avoiding intermediate HIN transformation and neighborhood attention. We provide theoretical analysis about the proposed ie-HGCN in terms of evaluating the usefulness of all possible meta-paths, its connection to the spectral graph convolution on HINs, and its quasi-linear time complexity. Extensive experiments on three real network datasets demonstrate the superiority of ie-HGCN over the state-of-the-art methods.