# Hybrid Low-order and Higher-order Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1908.00673 · HTML: https://mlsanity.com/paper/1908.00673/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability](https://mlsanity.com/paper/2406.09031/) (2024)
- [Relational Graph Neural Network Design via Progressive Neural Architecture Search](https://mlsanity.com/paper/2105.14490/) (2021)
- [GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials](https://mlsanity.com/paper/2209.15454/) (2022)
- [DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems](https://mlsanity.com/paper/2302.01443/) (2023)

## Abstract (arXiv)

With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher order graph convolutional network has a large number of parameters and high computational complexity. Therefore, we propose a Hybrid Lower order and Higher order Graph convolutional networks (HLHG) learning model, which uses weight sharing mechanism to reduce the number of network parameters. To reduce computational complexity, we propose a novel fusion pooling layer to combine the neighborhood information of high order and low order. Theoretically, we compare the model complexity of the proposed model with the other state-of-the-art model. Experimentally, we verify the proposed model on the large-scale text network datasets by supervised learning, and on the citation network datasets by semi-supervised learning. The experimental results show that the proposed model achieves highest classification accuracy with a small set of trainable weight parameters.